The document discusses a link mining methodology adapted from the CRISP-DM process to incorporate anomaly detection using mutual information. It applies this methodology in a case study of co-citation data. The methodology involves data description, preprocessing, transformation, exploration, modeling, and evaluation. Hierarchical clustering identified 5 clusters, with cluster 1 showing strong links and cluster 5 weak links. Mutual information validated the results, showing cluster 5 had the lowest mutual information, indicating independent variables. The case study demonstrated the approach can interpret anomalies semantically and be used with real-world data volumes and inconsistencies.
This document presents a novel approach to anomaly detection in link mining based on applying mutual information. It adapts the CRISP-DM methodology for link mining and applies it to a case study using co-citation data. The methodology includes data description, preprocessing, transformation, exploration, modeling through graph mapping and hierarchical clustering, and evaluation. Mutual information is used to interpret the semantics of anomalies identified in clusters. The case study identifies collective and community anomalies and confirms mutual information can validate clustering results by showing strong links within clusters but independence between objects in one cluster.
A SURVEY OF LINK MINING AND ANOMALIES DETECTIONIJDKP
This document discusses link mining and its application in detecting anomalies. It begins by defining link mining as focusing on discovering explicit links between objects, as opposed to data mining which aims to find patterns within datasets. The document then surveys different types of anomalies that can be detected through link mining, including contextual, point, collective, online, and distributed anomalies. It also discusses challenges in link mining like logical vs statistical dependencies and the skewed class distribution problem in link prediction. Applications of link mining mentioned include social networks, epidemiology, and bibliographic analysis. Overall, the document provides an overview of the emerging field of link mining and its relevance for detecting unusual or anomalous links within linked datasets.
A comprehensive survey of link mining and anomalies detectioncsandit
This document provides an overview of link mining and its application to anomalies detection. It discusses the emergence of link mining, key link mining tasks including object-related, graph-related and link-related tasks. Challenges of link mining are described along with applications. Different types of anomalies are defined and three main approaches to anomalies detection - supervised, semi-supervised and unsupervised - are outlined along with common methods like nearest neighbor, clustering, statistical and information-based approaches.
This document describes a proposed Optimal Frequent Patterns System (OFPS) that uses a genetic algorithm to discover optimal frequent patterns from transactional databases more efficiently. The OFPS is a three-fold system that first prepares data through cleaning, integration and transformation. It then constructs a Frequent Pattern Tree to discover frequent patterns. Finally, it applies a genetic algorithm to generate optimal frequent patterns, simulating biological evolution to find the best solutions. The proposed system aims to overcome limitations of conventional association rule mining approaches and efficiently discover optimal patterns from large, changing datasets.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
A Comparative Study on Privacy Preserving Datamining TechniquesIJMER
Privacy protection is very important in the recent years for the reason of increasing in the
ability to store data. In particular, recent advances in the data mining field have lead to increased
concerns about privacy. Data in its original form, however, typically contains sensitive information about
individuals, and publishing such data will violate individual privacy. The current practice in data
publishing based on that what type of data can be released and use of that data. Recently, PPDM has
received immersed attention in research communities, and many approaches have been proposed for
different data publishing scenarios. In this comparative study we will systematically summarize and
evaluate different approaches for PPDM, study the challenges ,differences and requirements that
distinguish PPDM from other related problems, and propose future research directions
Enhanced Privacy Preserving Accesscontrol in Incremental Datausing Microaggre...rahulmonikasharma
In microdata releases, main task is to protect the privacy of data subjects. Microaggregation technique use to disclose the limitation at protecting the privacy of microdata. This technique is an alternative to generalization and suppression, which use to generate k-anonymous data sets. In this dataset, identity of each subject is hidden within a group of k subjects. Microaggregation perturbs the data and additional masking allows refining data utility in many ways, like increasing data granularity, to avoid discretization of numerical data, to reduce the impact of outliers. If the variability of the private data values in a group of k subjects is too small, k-anonymity does not provide protection against attribute disclosure. In this work Role based access control is assumed. The access control policies define selection predicates to roles. Then use the concept of imprecision bound for each permission to define a threshold on the amount of imprecision that can be tolerated. So the proposed approach reduces the imprecision for each selection predicate. Anonymization is carried out only for the static relational table in the existing papers. Privacy preserving access control mechanism is applied to the incremental data.
PATTERN GENERATION FOR COMPLEX DATA USING HYBRID MININGIJDKP
This document discusses a hybrid data mining approach called combined mining that can generate informative patterns from complex data sources. It proposes applying three techniques: 1) Using the Lossy-counting algorithm on individual data sources to obtain frequent itemsets, 2) Generating incremental pair and cluster patterns using a multi-feature approach, 3) Combining FP-growth and Bayesian Belief Network using a multi-method approach to generate classifiers. The approach is tested on two datasets to obtain more useful knowledge and the results are compared.
This document presents a novel approach to anomaly detection in link mining based on applying mutual information. It adapts the CRISP-DM methodology for link mining and applies it to a case study using co-citation data. The methodology includes data description, preprocessing, transformation, exploration, modeling through graph mapping and hierarchical clustering, and evaluation. Mutual information is used to interpret the semantics of anomalies identified in clusters. The case study identifies collective and community anomalies and confirms mutual information can validate clustering results by showing strong links within clusters but independence between objects in one cluster.
A SURVEY OF LINK MINING AND ANOMALIES DETECTIONIJDKP
This document discusses link mining and its application in detecting anomalies. It begins by defining link mining as focusing on discovering explicit links between objects, as opposed to data mining which aims to find patterns within datasets. The document then surveys different types of anomalies that can be detected through link mining, including contextual, point, collective, online, and distributed anomalies. It also discusses challenges in link mining like logical vs statistical dependencies and the skewed class distribution problem in link prediction. Applications of link mining mentioned include social networks, epidemiology, and bibliographic analysis. Overall, the document provides an overview of the emerging field of link mining and its relevance for detecting unusual or anomalous links within linked datasets.
A comprehensive survey of link mining and anomalies detectioncsandit
This document provides an overview of link mining and its application to anomalies detection. It discusses the emergence of link mining, key link mining tasks including object-related, graph-related and link-related tasks. Challenges of link mining are described along with applications. Different types of anomalies are defined and three main approaches to anomalies detection - supervised, semi-supervised and unsupervised - are outlined along with common methods like nearest neighbor, clustering, statistical and information-based approaches.
This document describes a proposed Optimal Frequent Patterns System (OFPS) that uses a genetic algorithm to discover optimal frequent patterns from transactional databases more efficiently. The OFPS is a three-fold system that first prepares data through cleaning, integration and transformation. It then constructs a Frequent Pattern Tree to discover frequent patterns. Finally, it applies a genetic algorithm to generate optimal frequent patterns, simulating biological evolution to find the best solutions. The proposed system aims to overcome limitations of conventional association rule mining approaches and efficiently discover optimal patterns from large, changing datasets.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
A Comparative Study on Privacy Preserving Datamining TechniquesIJMER
Privacy protection is very important in the recent years for the reason of increasing in the
ability to store data. In particular, recent advances in the data mining field have lead to increased
concerns about privacy. Data in its original form, however, typically contains sensitive information about
individuals, and publishing such data will violate individual privacy. The current practice in data
publishing based on that what type of data can be released and use of that data. Recently, PPDM has
received immersed attention in research communities, and many approaches have been proposed for
different data publishing scenarios. In this comparative study we will systematically summarize and
evaluate different approaches for PPDM, study the challenges ,differences and requirements that
distinguish PPDM from other related problems, and propose future research directions
Enhanced Privacy Preserving Accesscontrol in Incremental Datausing Microaggre...rahulmonikasharma
In microdata releases, main task is to protect the privacy of data subjects. Microaggregation technique use to disclose the limitation at protecting the privacy of microdata. This technique is an alternative to generalization and suppression, which use to generate k-anonymous data sets. In this dataset, identity of each subject is hidden within a group of k subjects. Microaggregation perturbs the data and additional masking allows refining data utility in many ways, like increasing data granularity, to avoid discretization of numerical data, to reduce the impact of outliers. If the variability of the private data values in a group of k subjects is too small, k-anonymity does not provide protection against attribute disclosure. In this work Role based access control is assumed. The access control policies define selection predicates to roles. Then use the concept of imprecision bound for each permission to define a threshold on the amount of imprecision that can be tolerated. So the proposed approach reduces the imprecision for each selection predicate. Anonymization is carried out only for the static relational table in the existing papers. Privacy preserving access control mechanism is applied to the incremental data.
PATTERN GENERATION FOR COMPLEX DATA USING HYBRID MININGIJDKP
This document discusses a hybrid data mining approach called combined mining that can generate informative patterns from complex data sources. It proposes applying three techniques: 1) Using the Lossy-counting algorithm on individual data sources to obtain frequent itemsets, 2) Generating incremental pair and cluster patterns using a multi-feature approach, 3) Combining FP-growth and Bayesian Belief Network using a multi-method approach to generate classifiers. The approach is tested on two datasets to obtain more useful knowledge and the results are compared.
The document discusses data mining and knowledge discovery in databases. It defines data mining as the nontrivial extraction of implicit and potentially useful information from large amounts of data. With huge increases in data collection and storage, data mining aims to analyze data and discover patterns that can provide insights and knowledge about businesses and the real world. The data mining process involves selecting, preprocessing, transforming, and analyzing data to extract hidden patterns and relationships, which are then interpreted and evaluated.
A Trinity Construction for Web Extraction Using Efficient AlgorithmIOSR Journals
This document describes a proposed system for web extraction using a trinity construction and efficient algorithms. It begins with an abstract discussing how trinity characteristics can be used to automatically extract content from websites in a sequential tree structure. It then discusses the existing system which uses trinity tree and prefix/suffix sorting but has limitations. The proposed system introduces fuzzy logic for multi-perspective crawling across multiple websites. A genetic algorithm is used to load extracted content into the trinity structure and remove unwanted data. Finally, an ant colony optimization algorithm is used to obtain an effective structure and suggest optimized solutions.
Data mining is an integrated field, depicted technologies in combination to the areas having database, learning by machine, statistical study, and recognition in patterns of same type, information regeneration, A.I networks, knowledge-based portfolios, artificial intelligence, neural network, and data determination. In real terms, mining of data is the investigation of provisional data sets for finding hidden connections and to gather the information in peculiar form which are justifiable and understandable to the owner of gather or mined data. An unsupervised formula which differentiate data components into collections by which the components in similar group are more allied to one other and items in rest of cluster seems to be non-allied, by the criteria of measurement of equality or predictability is called process of clustering. Cluster analysis is a relegating task that is utilized to identify same group of object and it is additionally one of the most widely used method for many practical application in data mining. It is a method of grouping objects, where objects can be physical, such as a student or may be a summary such as customer comportment, handwriting. It has been proposed many clustering algorithms that it falls into the different clustering methods. The intention of this paper is to provide a relegation of some prominent clustering algorithms.
With the recent growth of the graph-based data, the large graph processing becomes more and more important. In order to explore and to extract knowledge from such data, graph mining methods, like community detection, is a necessity. The legacy graph processing tools mainly rely on single machine computational capacity, which cannot process large graphs with billions of nodes. Therefore, the main challenge of new tools and frameworks lies on the development of new paradigms that are scalable, efficient and flexible. In this paper, we review the new paradigms of large graph processing and their applications to graph mining domains using the distributed and shared nothing approach used for large data by Internet players.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The document discusses sampling techniques for online social networks. It proposes using an outlier indexing algorithm to sample large datasets from social networks. The key advantages of this approach are that random samples can be used for a wide range of analytical tasks and outlier detection. The paper also reviews related literature on estimating search tree sizes and sampling nodes in social networks. It then presents the proposed outlier indexing sampling algorithm for compressing social network structure and interest correlations across users.
Novel modelling of clustering for enhanced classification performance on gene...IJECEIAES
Gene expression data is popularized for its capability to disclose various disease conditions. However, the conventional procedure to extract gene expression data itself incorporates various artifacts that offer challenges in diagnosis a complex disease indication and classification like cancer. Review of existing research approaches indicates that classification approaches are few to proven to be standard with respect to higher accuracy and applicable to gene expression data apart from unaddresed problems of computational complexity. Therefore, the proposed manuscript introduces a novel and simplified model capable using Graph Fourier Transform, Eigen Value and vector for offering better classification performance considering case study of microarray database, which is one typical example of gene expressiondata. The study outcome shows that proposed system offers comparatively better accuracy and reduced computational complexity with the existing clustering approaches.
A Survey On Ontology Agent Based Distributed Data MiningEditor IJMTER
With the increased complexity in number of applications and due to large volume
of availability of data from heterogeneous sources, there is a need for the development of
suitable ontology, which can handle the large data set and present the mined outcomes for
evaluation intelligently. In the era of intensive data driven applications distributed data mining can
meet the challenges with the support of agents. This paper discusses the underlying principles for
effectiveness of modern agent-based systems for distributed data mining
This document discusses research challenges in data mining for science and engineering. It covers challenges related to information network analysis, discovery and understanding of patterns from data, stream data mining, and other topics. Key points discussed include analyzing complex networks in scientific domains, developing methods for mining long and approximate patterns, and designing algorithms that can handle streaming data.
Graph mining analyzes structured data like social networks and the web through graph search algorithms. It aims to find frequent subgraphs using Apriori-based or pattern growth approaches. Social networks exhibit characteristics like densification and heavy-tailed degree distributions. Link mining analyzes heterogeneous, multi-relational social network data through tasks like link prediction and group detection, facing challenges of logical vs statistical dependencies and collective classification. Multi-relational data mining searches for patterns across multiple database tables, including multi-relational clustering that utilizes information across relations.
A Comparative Study of Various Data Mining Techniques: Statistics, Decision T...Editor IJCATR
In this paper we focus on some techniques for solving data mining tasks such as: Statistics, Decision Trees and Neural
Networks. The new approach has succeed in defining some new criteria for the evaluation process, and it has obtained valuable results
based on what the technique is, the environment of using each techniques, the advantages and disadvantages of each technique, the
consequences of choosing any of these techniques to extract hidden predictive information from large databases, and the methods of
implementation of each technique. Finally, the paper has presented some valuable recommendations in this field.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
There are numerous ways to analyse the web information, generally web substance are housed in
large information sets and basic inquiries are utilized to parse such information sets. As the requests
expanded with time, mining web information amended to meet challenging task in a web analysis.
Machine learning methodologies are the most up to date one to go into these analysis forms. Different
approaches like decision trees, association rules, Meta heuristic and basic learning methods are embraced
for making web data appraisal and mining data from various web instances. This study will highlight these
approaches in perspective of web investigation. One of the prime goals of this exploration is to investigate
more data mining approaches alongside machine learning systems, and to express emerging collaboration
of web analytics with artificial intelligence.
Drug discovery and development is a long and expensive process and over time has notoriously bucked Moore’s law that it now has its own law called Eroom’s Law named after it (the opposite of Moore’s). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of the failures.
Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible is paramount in accelerating drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains.
Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories:
1. Discovery,
2. Toxicity and Safety, and
3. Post-Market Monitoring.
We will address the recent progress in predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them.
We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will address some of the remaining challenges and limitations yet to be addressed in the area of drug discovery and safety assessment.
APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN ESTIMATING PARTICIPATION IN ELEC...Zac Darcy
This document discusses using artificial neural networks to estimate voter participation rates in future elections in Iran. Specifically, it describes using a two-layer feed-forward neural network to predict voter turnout in the Kohgiluyeh and Boyer-Ahmad province with 91% accuracy. The neural network was trained on past electoral data from the province. The document also provides background on artificial neural networks and reviews their use in predicting outcomes in various domains, including economics, politics, tourism, the environment, and information technology.
Recent Trends in Incremental Clustering: A ReviewIOSRjournaljce
This document provides a review of recent trends in incremental clustering algorithms. It discusses clustering methods based on both similarity measures and those not based on similarity measures. Specific incremental clustering algorithms covered include single-pass clustering, k-nearest neighbors clustering, suffix tree clustering, incremental DBSCAN, and ICIB (incremental clustering based on information bottleneck theory). The document also reviews various techniques for clustering, including particle swarm optimization, ant colony optimization, and genetic algorithms. Applications of genetic algorithm based clustering are discussed.
Simplicial closure & higher-order link predictionAustin Benson
The document discusses higher-order link prediction in networks. It summarizes previous work representing higher-order interactions as tensors, hypergraphs, etc. It then proposes evaluating models of higher-order data using "higher-order link prediction" to predict which groups of more than two nodes will interact based on past data. The authors analyze dynamics of triadic closure in several real-world networks and propose methods to predict closure based on structural properties like edge weights.
TUPLE VALUE BASED MULTIPLICATIVE DATA PERTURBATION APPROACH TO PRESERVE PRIVA...IJDKP
Huge volume of data from domain specific applications such as medical, financial, library, telephone,
shopping records and individual are regularly generated. Sharing of these data is proved to be beneficial
for data mining application. On one hand such data is an important asset to business decision making by
analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information
for data analysis. In order to share data while preserving privacy, data owner must come up with a solution
which achieves the dual goal of privacy preservation as well as an accuracy of data mining task –
clustering and classification. An efficient and effective approach has been proposed that aims to protect
privacy of sensitive information and obtaining data clustering with minimum information loss
IRJET-Efficient Data Linkage Technique using one Class Clustering Tree for Da...IRJET Journal
This document proposes a new one-to-many data linkage technique using a One-Class Clustering Tree (OCCT) to link records from different datasets. The technique constructs a decision tree where internal nodes represent attributes from the first dataset and leaves represent attributes from the second dataset that match. It uses maximum likelihood estimation for splitting criteria and pre-pruning to reduce complexity. The method is applied to the database misuse domain to identify common and malicious users by analyzing access request contexts and accessible data. Evaluation shows the technique achieves better precision and recall than existing methods.
Privacy preservation techniques in data miningeSAT Journals
Abstract In this paper different privacy preservation techniques are compared. Classification is the most commonly applied data mining technique, which employs a set of pre-classified examples to develop a model that can classify the population of records at large. Fraud detection and credit risk applications are particularly well suited to this type of analysis. This approach frequently employs decision tree or neural network-based classification algorithms. The data classification process involves learning and classification. In Learning the training data are analyzed by classification algorithm. In classification test data are used to estimate the accuracy of the classification rules. If the accuracy is acceptable the rules can be applied to the new data tuples . For a fraud detection application, this would include complete records of both fraudulent and valid activities determined on a record-by-record basis. The classifier-training algorithm uses these pre-classified examples to determine the set of parameters required for proper discrimination. The algorithm then encodes these parameters into a model called a classifier Index Terms: Data Mining, Privacy Preservation, Clustering, Classification Techniques, Naive Bayes.
This document discusses various privacy preservation techniques in data mining. It summarizes classification, clustering, and association rule learning as common privacy preservation approaches. For classification, it describes decision trees, k-nearest neighbors, artificial neural networks, support vector machines, and naive Bayes models. It provides advantages and disadvantages of these techniques. The document concludes that privacy preservation techniques have emerged to allow for efficient and effective data mining while protecting sensitive data.
The document discusses data mining and knowledge discovery in databases. It defines data mining as the nontrivial extraction of implicit and potentially useful information from large amounts of data. With huge increases in data collection and storage, data mining aims to analyze data and discover patterns that can provide insights and knowledge about businesses and the real world. The data mining process involves selecting, preprocessing, transforming, and analyzing data to extract hidden patterns and relationships, which are then interpreted and evaluated.
A Trinity Construction for Web Extraction Using Efficient AlgorithmIOSR Journals
This document describes a proposed system for web extraction using a trinity construction and efficient algorithms. It begins with an abstract discussing how trinity characteristics can be used to automatically extract content from websites in a sequential tree structure. It then discusses the existing system which uses trinity tree and prefix/suffix sorting but has limitations. The proposed system introduces fuzzy logic for multi-perspective crawling across multiple websites. A genetic algorithm is used to load extracted content into the trinity structure and remove unwanted data. Finally, an ant colony optimization algorithm is used to obtain an effective structure and suggest optimized solutions.
Data mining is an integrated field, depicted technologies in combination to the areas having database, learning by machine, statistical study, and recognition in patterns of same type, information regeneration, A.I networks, knowledge-based portfolios, artificial intelligence, neural network, and data determination. In real terms, mining of data is the investigation of provisional data sets for finding hidden connections and to gather the information in peculiar form which are justifiable and understandable to the owner of gather or mined data. An unsupervised formula which differentiate data components into collections by which the components in similar group are more allied to one other and items in rest of cluster seems to be non-allied, by the criteria of measurement of equality or predictability is called process of clustering. Cluster analysis is a relegating task that is utilized to identify same group of object and it is additionally one of the most widely used method for many practical application in data mining. It is a method of grouping objects, where objects can be physical, such as a student or may be a summary such as customer comportment, handwriting. It has been proposed many clustering algorithms that it falls into the different clustering methods. The intention of this paper is to provide a relegation of some prominent clustering algorithms.
With the recent growth of the graph-based data, the large graph processing becomes more and more important. In order to explore and to extract knowledge from such data, graph mining methods, like community detection, is a necessity. The legacy graph processing tools mainly rely on single machine computational capacity, which cannot process large graphs with billions of nodes. Therefore, the main challenge of new tools and frameworks lies on the development of new paradigms that are scalable, efficient and flexible. In this paper, we review the new paradigms of large graph processing and their applications to graph mining domains using the distributed and shared nothing approach used for large data by Internet players.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The document discusses sampling techniques for online social networks. It proposes using an outlier indexing algorithm to sample large datasets from social networks. The key advantages of this approach are that random samples can be used for a wide range of analytical tasks and outlier detection. The paper also reviews related literature on estimating search tree sizes and sampling nodes in social networks. It then presents the proposed outlier indexing sampling algorithm for compressing social network structure and interest correlations across users.
Novel modelling of clustering for enhanced classification performance on gene...IJECEIAES
Gene expression data is popularized for its capability to disclose various disease conditions. However, the conventional procedure to extract gene expression data itself incorporates various artifacts that offer challenges in diagnosis a complex disease indication and classification like cancer. Review of existing research approaches indicates that classification approaches are few to proven to be standard with respect to higher accuracy and applicable to gene expression data apart from unaddresed problems of computational complexity. Therefore, the proposed manuscript introduces a novel and simplified model capable using Graph Fourier Transform, Eigen Value and vector for offering better classification performance considering case study of microarray database, which is one typical example of gene expressiondata. The study outcome shows that proposed system offers comparatively better accuracy and reduced computational complexity with the existing clustering approaches.
A Survey On Ontology Agent Based Distributed Data MiningEditor IJMTER
With the increased complexity in number of applications and due to large volume
of availability of data from heterogeneous sources, there is a need for the development of
suitable ontology, which can handle the large data set and present the mined outcomes for
evaluation intelligently. In the era of intensive data driven applications distributed data mining can
meet the challenges with the support of agents. This paper discusses the underlying principles for
effectiveness of modern agent-based systems for distributed data mining
This document discusses research challenges in data mining for science and engineering. It covers challenges related to information network analysis, discovery and understanding of patterns from data, stream data mining, and other topics. Key points discussed include analyzing complex networks in scientific domains, developing methods for mining long and approximate patterns, and designing algorithms that can handle streaming data.
Graph mining analyzes structured data like social networks and the web through graph search algorithms. It aims to find frequent subgraphs using Apriori-based or pattern growth approaches. Social networks exhibit characteristics like densification and heavy-tailed degree distributions. Link mining analyzes heterogeneous, multi-relational social network data through tasks like link prediction and group detection, facing challenges of logical vs statistical dependencies and collective classification. Multi-relational data mining searches for patterns across multiple database tables, including multi-relational clustering that utilizes information across relations.
A Comparative Study of Various Data Mining Techniques: Statistics, Decision T...Editor IJCATR
In this paper we focus on some techniques for solving data mining tasks such as: Statistics, Decision Trees and Neural
Networks. The new approach has succeed in defining some new criteria for the evaluation process, and it has obtained valuable results
based on what the technique is, the environment of using each techniques, the advantages and disadvantages of each technique, the
consequences of choosing any of these techniques to extract hidden predictive information from large databases, and the methods of
implementation of each technique. Finally, the paper has presented some valuable recommendations in this field.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
There are numerous ways to analyse the web information, generally web substance are housed in
large information sets and basic inquiries are utilized to parse such information sets. As the requests
expanded with time, mining web information amended to meet challenging task in a web analysis.
Machine learning methodologies are the most up to date one to go into these analysis forms. Different
approaches like decision trees, association rules, Meta heuristic and basic learning methods are embraced
for making web data appraisal and mining data from various web instances. This study will highlight these
approaches in perspective of web investigation. One of the prime goals of this exploration is to investigate
more data mining approaches alongside machine learning systems, and to express emerging collaboration
of web analytics with artificial intelligence.
Drug discovery and development is a long and expensive process and over time has notoriously bucked Moore’s law that it now has its own law called Eroom’s Law named after it (the opposite of Moore’s). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of the failures.
Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible is paramount in accelerating drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains.
Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories:
1. Discovery,
2. Toxicity and Safety, and
3. Post-Market Monitoring.
We will address the recent progress in predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them.
We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will address some of the remaining challenges and limitations yet to be addressed in the area of drug discovery and safety assessment.
APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN ESTIMATING PARTICIPATION IN ELEC...Zac Darcy
This document discusses using artificial neural networks to estimate voter participation rates in future elections in Iran. Specifically, it describes using a two-layer feed-forward neural network to predict voter turnout in the Kohgiluyeh and Boyer-Ahmad province with 91% accuracy. The neural network was trained on past electoral data from the province. The document also provides background on artificial neural networks and reviews their use in predicting outcomes in various domains, including economics, politics, tourism, the environment, and information technology.
Recent Trends in Incremental Clustering: A ReviewIOSRjournaljce
This document provides a review of recent trends in incremental clustering algorithms. It discusses clustering methods based on both similarity measures and those not based on similarity measures. Specific incremental clustering algorithms covered include single-pass clustering, k-nearest neighbors clustering, suffix tree clustering, incremental DBSCAN, and ICIB (incremental clustering based on information bottleneck theory). The document also reviews various techniques for clustering, including particle swarm optimization, ant colony optimization, and genetic algorithms. Applications of genetic algorithm based clustering are discussed.
Simplicial closure & higher-order link predictionAustin Benson
The document discusses higher-order link prediction in networks. It summarizes previous work representing higher-order interactions as tensors, hypergraphs, etc. It then proposes evaluating models of higher-order data using "higher-order link prediction" to predict which groups of more than two nodes will interact based on past data. The authors analyze dynamics of triadic closure in several real-world networks and propose methods to predict closure based on structural properties like edge weights.
TUPLE VALUE BASED MULTIPLICATIVE DATA PERTURBATION APPROACH TO PRESERVE PRIVA...IJDKP
Huge volume of data from domain specific applications such as medical, financial, library, telephone,
shopping records and individual are regularly generated. Sharing of these data is proved to be beneficial
for data mining application. On one hand such data is an important asset to business decision making by
analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information
for data analysis. In order to share data while preserving privacy, data owner must come up with a solution
which achieves the dual goal of privacy preservation as well as an accuracy of data mining task –
clustering and classification. An efficient and effective approach has been proposed that aims to protect
privacy of sensitive information and obtaining data clustering with minimum information loss
IRJET-Efficient Data Linkage Technique using one Class Clustering Tree for Da...IRJET Journal
This document proposes a new one-to-many data linkage technique using a One-Class Clustering Tree (OCCT) to link records from different datasets. The technique constructs a decision tree where internal nodes represent attributes from the first dataset and leaves represent attributes from the second dataset that match. It uses maximum likelihood estimation for splitting criteria and pre-pruning to reduce complexity. The method is applied to the database misuse domain to identify common and malicious users by analyzing access request contexts and accessible data. Evaluation shows the technique achieves better precision and recall than existing methods.
Privacy preservation techniques in data miningeSAT Journals
Abstract In this paper different privacy preservation techniques are compared. Classification is the most commonly applied data mining technique, which employs a set of pre-classified examples to develop a model that can classify the population of records at large. Fraud detection and credit risk applications are particularly well suited to this type of analysis. This approach frequently employs decision tree or neural network-based classification algorithms. The data classification process involves learning and classification. In Learning the training data are analyzed by classification algorithm. In classification test data are used to estimate the accuracy of the classification rules. If the accuracy is acceptable the rules can be applied to the new data tuples . For a fraud detection application, this would include complete records of both fraudulent and valid activities determined on a record-by-record basis. The classifier-training algorithm uses these pre-classified examples to determine the set of parameters required for proper discrimination. The algorithm then encodes these parameters into a model called a classifier Index Terms: Data Mining, Privacy Preservation, Clustering, Classification Techniques, Naive Bayes.
This document discusses various privacy preservation techniques in data mining. It summarizes classification, clustering, and association rule learning as common privacy preservation approaches. For classification, it describes decision trees, k-nearest neighbors, artificial neural networks, support vector machines, and naive Bayes models. It provides advantages and disadvantages of these techniques. The document concludes that privacy preservation techniques have emerged to allow for efficient and effective data mining while protecting sensitive data.
The past two decades has seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation of data has taken place at an explosive rate. It has been estimated that the amount of information in the world doubles every 20 months and the size and number of databases are increasing even faster. The increase in use of electronic data gathering devices such as point-of-sale or remote sensing devices has contributed to this explosion of available data. Figure 1 from the Red Brick company illustrates the data explosion.
Data mining is utilized to manage huge measure of information which are put in the data ware houses and databases, to discover required information and data. Numerous data mining systems have been proposed, for example, association rules, decision trees, neural systems, clustering, and so on. It has turned into the purpose of consideration from numerous years. A re-known amongst the available data mining strategies is clustering of the dataset. It is the most effective data mining method. It groups the dataset in number of clusters based on certain guidelines that are predefined. It is dependable to discover the connection between the distinctive characteristics of data.
In k-mean clustering algorithm, the function is being selected on the basis of the relevancy of the function for predicting the data and also the Euclidian distance between the centroid of any cluster and the data objects outside the cluster is being computed for the clustering the data points. In this work, author enhanced the Euclidian distance formula to increase the cluster quality.
The problem of accuracy and redundancy of the dissimilar points in the clusters remains in the improved k-means for which new enhanced approach is been proposed which uses the similarity function for checking the similarity level of the point before including it to the cluster.
Using Randomized Response Techniques for Privacy-Preserving Data Mining14894
This document proposes using randomized response techniques to conduct privacy-preserving data mining and build decision tree classifiers from disguised data. It presents a method called Multivariate Randomized Response (MRR) that extends randomized response to handle multiple attributes. Experiments show that while the data is disguised, decision trees built from it can still achieve high accuracy compared to trees built from original data, if the randomization parameter is chosen appropriately. The accuracy is affected by this randomization parameter.
This document provides an overview of nature-inspired methods that have been used in the Semantic Web for tasks like information retrieval, extraction, clustering, and personalization. It discusses how genetic algorithms, neural networks, fuzzy logic, and rough sets have helped with problems in these areas by modeling complex relationships and uncertainty. The document also describes approaches for representing uncertainty in ontologies, including using Bayesian networks to quantify overlap between concepts.
This document reviews the use of data mining and neural network techniques for stock market prediction. It discusses how data mining can extract hidden patterns from large datasets and neural networks can handle nonlinear and uncertain financial data. Specifically, it examines how a combination of data mining and neural networks may improve the reliability of stock predictions by leveraging their complementary strengths. The document also provides an overview of common data mining and neural network methods used for this purpose, such as statistical data mining, neural network-based data processing, clustering, and fuzzy logic. It reviews several previous studies that found neural networks and other nonlinear techniques often outperform traditional statistical models at predicting stock prices and indices.
This document reviews the use of data mining and neural network techniques for stock market prediction. It discusses how data mining can extract hidden patterns from large datasets and make predictions about future trends. Neural networks are also effective for stock prediction due to their ability to handle uncertain and changing data. The document examines different data mining methods like statistical analysis, neural networks, clustering and fuzzy sets. It suggests that combining data mining and neural networks could improve the reliability of stock market predictions by uncovering the nonlinear patterns in stock price data.
1) The document discusses using k-means clustering to analyze big data. K-means is an algorithm that partitions data into k clusters based on similarity.
2) It provides background on big data characteristics like volume, variety, and velocity. It also discusses challenges of heterogeneous, decentralized, and evolving data.
3) The document proposes applying k-means clustering to big data to map data into clusters according to its properties in a fast and efficient manner. This allows statistical analysis and knowledge extraction from large, complex datasets.
This document summarizes a research paper on using k-means clustering to analyze big data. It begins with an introduction to big data and its characteristics. It then discusses related work on big data storage, mining, and analytics. The HACE theorem for defining big data is presented. The k-means clustering algorithm is explained as an efficient method for partitioning big data into groups. The proposed system uses k-means clustering followed by data mining and classification modules. Experimental results on two datasets show that the recursive k-means approach finds clusters closer to the actual number than the iterative approach. In conclusion, clustering is effective for handling big data attributes like heterogeneity and complexity, and k-means distribution helps distribute data into appropriate clusters.
This document compares two approaches for handling incomplete data and generating decision rules: 1) Rough set theory, which fills missing values and performs attribute reduction, and 2) Random tree classification in data mining, which ignores missing values. It uses a heart disease dataset with missing values to test the approaches in ROSE2 and WEKA. The results show that random tree classification ignoring missing values produces more accurate decision rules than rough set theory filling missing values.
Indexing based Genetic Programming Approach to Record Deduplicationidescitation
In this paper, we present a genetic programming (GP) approach to record
deduplication with indexing techniques.Data de-duplication is a process in which data are
cleaned from duplicate records due to misspelling, field swap or any other mistake or data
inconsistency. This process requires that we identify objects that are included in more than
one list.The problem of detecting and eliminating duplicated data is one of the major
problems in the broad area of data cleaning and data quality in data warehouse. So, we
need to create such a algorithm that can detect and eliminate maximum duplications.GP
with indexing is one of the optimization technique that helps to find maximum duplicates in
the database. We used adeduplication function that is able to identify whether two or more
entries in a repository are replicas or not. As many industries and systems depend on the
accuracy and reliability of databases to carry out operations. Therefore, the quality of the
information stored in the databases, can have significant cost implications to a system that
relies on information to function and conduct business. Moreover, this is fact that clean and
replica-free repositories not only allow the retrieval of higher quality information but also
lead to more concise data and to potential savings in computational time and resources to
process this data.
Index
Medical informatics growth can be observed now days. Advancement in different medical fields
discovers the various critical diseases and provides the guidelines for their cure. This has been possible
only because of well heeled medical databases as well as automation of data analysis process. Towards
this analysis process lots of learning and intelligence is required, the data mining techniques provides the
basis for that and various data mining techniques are available like Decision tree Induction, Rule Based
Classification or mining, Support vector machine, Stochastic classification, Logistic regression, Naïve
bayes, Artificial Neural Network & Fuzzy Logic, Genetic Algorithms. This paper provides the basic of
data mining with their effective techniques availability in medical sciences & reveals the efforts done on
medical databases using data mining techniques for human disease diagnosis.
Classifier Model using Artificial Neural NetworkAI Publications
This document summarizes a research paper that investigates using supervised instance selection (SIS) as a preprocessing step to improve the performance of artificial neural networks (ANNs) for classification tasks. SIS aims to select a subset of examples from the original dataset to enhance the accuracy of future classifications. The goal of applying SIS before ANNs is to provide a cleaner input dataset that handles noisy or redundant data better. The paper presents the architecture of feedforward neural networks and the backpropagation algorithm for training networks. It also discusses using mutual information-based feature selection as part of the SIS preprocessing approach.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Frequent Item set Mining of Big Data for Social MediaIJERA Editor
Big data is a term for massive data sets having large, more varied and complex structure with the difficulties of storing, analyzing and visualizing for further processes or results. Bigdata includes data from email, documents, pictures, audio, video files, and other sources that do not fit into a relational database. This unstructured data brings enormous challenges to Bigdata.The process of research into massive amounts of data to reveal hidden patterns and secret correlations named as big data analytics. Therefore, big data implementations need to be analyzed and executed as accurately as possible. The proposed model structures the unstructured data from social media in a structured form so that data can be queried efficiently by using Hadoop MapReduce framework. The Bigdata mining is essential in order to extract value from massive amount of data. MapReduce is efficient method to deal with Big data than traditional techniques.The proposed Linguistic string matching Knuth-Morris-Pratt algorithm and K-Means clustering algorithm gives proper platform to extract value from massive amount of data and recommendation for user.Linguistic matching techniques such as Knuth–Morris–Pratt string matching algorithm are very useful in giving proper matching output to user query. The K-Means algorithm is one which works on clustering data using vector space model. It can be an appropriate method to produce recommendation for user.
Frequent Item set Mining of Big Data for Social MediaIJERA Editor
Big data is a term for massive data sets having large, more varied and complex structure with the difficulties of storing, analyzing and visualizing for further processes or results. Bigdata includes data from email, documents, pictures, audio, video files, and other sources that do not fit into a relational database. This unstructured data brings enormous challenges to Bigdata.The process of research into massive amounts of data to reveal hidden patterns and secret correlations named as big data analytics. Therefore, big data implementations need to be analyzed and executed as accurately as possible. The proposed model structures the unstructured data from social media in a structured form so that data can be queried efficiently by using Hadoop MapReduce framework. The Bigdata mining is essential in order to extract value from massive amount of data. MapReduce is efficient method to deal with Big data than traditional techniques.The proposed Linguistic string matching Knuth-Morris-Pratt algorithm and K-Means clustering algorithm gives proper platform to extract value from massive amount of data and recommendation for user.Linguistic matching techniques such as Knuth–Morris–Pratt string matching algorithm are very useful in giving proper matching output to user query. The K-Means algorithm is one which works on clustering data using vector space model. It can be an appropriate method to produce recommendation for user
Clustering heterogeneous categorical data using enhanced mini batch K-means ...IJECEIAES
This document presents a proposed framework called MBKEM (Mini Batch K-means with Entropy Measure) for clustering heterogeneous categorical data. MBKEM uses an entropy distance measure within a mini batch k-means algorithm. The framework is evaluated using secondary data from a public survey. Evaluation metrics show MBKEM outperforms other clustering algorithms with high accuracy, v-measure, adjusted rand index, and Fowlkes-Mallow's index. MBKEM also has faster average cluster generation time than other methods. The proposed framework provides an improved solution for clustering heterogeneous categorical data.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
1. International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.7, No.3, May 2017
DOI: 10.5121/ijdkp.2017.7304 45
LINK MINING PROCESS
Dr.Zakea Il-Agure and Mr.Hicham Noureddine Itani
Higher Colleges of Technology, United Arab Emirates
ABSTRACT
Many data mining and knowledge discovery methodologies and process models have been developed,
with varying degrees of success, there are three main methods used to discover patterns in data; KDD,
SEMMA and CRISP-DM. They are presented in many of the publications of the area and are used in
practice. To our knowledge, there is no clear methodology developed to support link mining. However,
there is a well known methodology in knowledge discovery in databases, known as Cross Industry
Standard Process for Data Mining (CRISPDM), developed by a consortium of several industrial
companies which can be relevant to the study of link mining. In this study CRISP-DM has been adapted to
the field of Link mining to detect anomalies. An important goal in link mining is the task of inferring links
that are not yet known in a given network. This approach is implemented through the use of a case study
of realworld data (co-citation data). This case study aims to use mutual information to interpret the
semantics of anomalies identified in co-citation, dataset that can provide valuable insights in determining
the nature of a given link and potentially identifying important future link relationships.
KEYWORDS
Link mining, anomalies, mutual information
1. INTRODUCTION
Link mining is a new emerging research area, which differs from data mining. Whilst data mining
aims at discovering new potentially hidden patterns in datasets, link mining considers datasets as
a linked collection of interrelated objects and therefore it focuses on discovering explicit links
between objects. A crucial step in both data and link mining is to ensure that the analysis is
undertaken on reliable, robust and efficient data, and to identify outliers, which are observations
that are numerically distant from the rest of the data. Reliability of detection anomaly should
achieve high data delivery reliability unless the quality of the underlying links makes that
infeasible. Robustness should be robust against huge or complex social networks failures,
dynamic networks, and topology changes. In spite of these dynamics, it should function without
much tuning or configuration. Efficiency in communication often applies both complex
anomalies and different types of anomalies, to allow an opportunity to make the method detection
anomalies more efficient. Though outliers are often considered as an error or noise in data
mining, they are often referred to as anomalies in link mining as they can carry important
information. Often the data contains noise that tends to be similar to the actual anomalies and
hence it is difficult to distinguish and remove them (Chandola et al.,2009). Any errors in data are
to be examined taking into consideration the context of the domains; some may be true errors and
therefore removed, whereas other errors may be regarded as interesting anomalies.
2. International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.7, No.3, May 2017
46
In the last decade we have seen an increasing interest in the study of anomalies detection in data
mining applied to law enforcement, financial fraud, and terrorism. In recent years, this study has
been applied to social networks and online communities to identify influential networks
participants and predict fraudulent or malicious activities.
To our knowledge, the study of anomaly detection in link mining relied mostly on statistical or
machine learning methods in order to gain insight to the structure of their networks. We believe
that we can achieve a better understanding of these anomalies if we apply mutual information to
the data entities and objects and links to reveal their sematic relationship. The aim of this research
is to show how mutual information can help in providing a semantic interpretation of anomalies
in data, to characterise the anomalies, and how mutual information can help measuring the
information that object item X shares with another object item Y. This paper attempted to
demonstrate the contribution of mutual information to interpret anomalies using a case study.
This paper presents a novel approach to anomaly detection in link mining methodology based on
mutual information.
2. LINK MINING METHODOLOGY
As CRISP–DM methodology is well developed and applied in knowledge discovery; this
research has adapted it to the emerging field of link mining. While data mining addresses the
discovery of patterns in data entities, link mining is interested in finding patterns in objects by
exploiting and modeling the link among the objects. The approach to link mining is still an ad-
hoc approach. The proposed adopted CRISP-DM methodology can help provide a structured
approach to link mining in Figure 1. This consists of six stages:
Figure 1. Link mining methodology
The aim of this methodology is to define the link mining task and determining the objectives of
link mining.
1. Data description. The data description phase starts with initial data collection and
proceeds with activities that enable the researcher to become familiar with the data. The
aim is to check data quality and any associated problems in order to discover first insights
into the data, and identify interesting subsets to form hypotheses regarding hidden
information.
2. Data pre-processing. The data pre-processing phase covers activities related to data
cleansing and data integrity needed to construct the final dataset from the initial raw data.
While outliers can be considered noise, or anomalies and thus discarded in data mining,
they become the focus of this study as they can reveal important knowledge in link
mining.
3. International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.7, No.3, May 2017
47
3. Data transformation. This involves syntactic modifications applied to the data; this may
be required by the modelling tool. Selecting an appropriate representation is an important
challenge in link mining. The objects in link mining (e.g. people, events, organisation,
and countries) have to be transformed into feature factors to represent and capture the
connectivity and the strength of the links among those objects.
4. Data exploration. This stage is concerned with the distribution of the data and using
relevant graphical tools to visualise the structure of the objects and their links. This stage
helps identify the existence of anomalous objects or links.
5. Data modelling. This stage aims to identify all entities and the relationship between them.
Data modeling puts algorithm in general in a historical perspective rooted in
mathematics, statistics, and numerical analysis. For more complex data sets, different
techniques are used such as nearest neighbour, statistical, classification, and information/
context based approaches.
6. Evaluation: Data cleaning solutions will clean data by cross checking with a validated
data set in phase 2. The clustering model in phase 5, explains natural groupings within a
dataset based on a set of input variables. The resulting clustering model is sufficient
statistics for calculating the cluster group norms and anomaly indices. Mutual
information is useful in validating the model as it provides a semantic underpinning to
the patterns and discoveries made in phase 5.
3. CASE STUDY
The application of the novel approach is implemented to a case study to demonstrate how mutual
information can help explore and interpret anomalies detection with a real-world data set and
application area. The key challenge for this technique is to apply data representation, for example
graphs to visualise the dataset and a clustering approach (hierarchical cluster method). In Figure 2
shows how this study focuses on a case study using a set of co-citation data. The link mining
methodology described above is applied to this case study and includes the following stages: data
description, data preprocessing, data transformation, data exploration, data modeling based on
graph mapping, hierarchical cluster and visualisation, and data evaluation.
Figure 2. Link mining methodology in case study
4. International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.7, No.3, May 2017
48
This case study covers the three link mining tasks. It is an attempt at identifying and clustering
objects, representing them into a graph structure and studying the links between these objects.
4. DISCUSSION
If the approach were to be valid when used with a data set where the anomalies and relationships
are unknown, it is necessary to demonstrate that the approach could be scaled to real world data
volumes and used with inconsistent and/or noisy data and with other clustering algorithms. This
case study addresses these issues. The clustering approach used in this case study was
hierarchical clustering. Using bibliographic data, this approach created 5 clusters. Cluster 1 was
found to contain data with the strongest links and cluster 5 to contain data with the weakest links.
Applying mutual information, we were able to demonstrate that the clusters created by applying
the algorithm reflected the semantics of the data. Cluster 5 contained the data with the lowest
mutual information calculation value. This demonstrated that mutual information could be used to
validate the results of the clustering algorithm.
As the result in Table 1, cluster 1 shows high mutual information indicating higher co-citation
strength; cluster 5 has low mutual information indicating lower co-citation strength.
Table 1. Result of mutual information
It was necessary to establish whether the proposed approach would be valid if used with a data set
where the anomalies and relationships were unknown. Having clustered and then visualized the
data and examined the resulting visualisation graph and the underlying cluster through mutual
information, we were able to determine that the results produced were valid, demonstrating that
the approach can be used with the real world data set. Analyzing each of the clusters, and the
relationships between elements in the clusters was time consuming but enabled us to establish
that the approach could be scaled to real world data and that it could be used with anomalies
which were previously unknown. We found with the case study that the semantic preprocessing
stage was an essential first step. The data from the bibliographic sources normally contains errors,
such as misspelling the author’s name, the journal title, or in the references list. Occasionally,
additional information has to be added to the original data, for example, if the author’s address is
incomplete or wrong. For this reason, the analysis cannot be applied directly to the data retrieved
from the bibliographic sources -a pre-processing stage over the retrieved data is necessary to
Overcome these issues. In this case study, the clustering approach was used to cluster the data
into groups sharing common characteristics, graph based visualization and mutual information
was used to validate the approach.
5. International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.7, No.3, May 2017
49
Figure 3. Mapping nodes
Clusters are designed to classify observations, as anomalies should fall in regions of the data
space where there is a small density of normal observations. The anomalies occur in this case
study as a cluster among the data, such observations are called collective anomalies, defined by
Chandola et al. (2009) as follows: “The individual data instances in a collective anomaly may not
be anomalies by themselves, but their occurrence together, as a collection is anomalous.” Existing
work on collective anomaly detection requires supporting relationships to connect the
observations, such as sequential data, spatial data and graph data. Mutual information can be used
to interpret collective anomalies. Mutual information can contribute to our understanding of
anomalous features and help to identify links with anomalous behaviour. In this case study,
mutual information was applied to interpret the semantics of the clusters. In cluster 5, for
example, mutual information found no links amongst this group of nodes. This indicates
collective anomalies , as zero mutual information between two random variables means that the
variables are independent. Link mining considers data sets as a linked collection of interrelated
objects and therefore it focuses on discovering explicit links between objects. Using mutual
information allows us to work with objects without these explicit links. Cluster 5 contained
documents, which had been selected as part of the co-citation data, but these documents were not
themselves cited. Mutual information allowed us to examine the relationships between documents
and to determine that some objects made use of self-citation meaning that they were regarded co-
cited but did not connect to other objects. We also identified a community anomaly, where the
edge is considered a relationship anomaly, because it connects two communities, which are
usually not connected to one another. Mutual information provided information about the
relationships between objects, which could not be inferred from a clustering approach alone. This
additional information supports a semantic explanation of anomalies.
The co-citation data applied hierarchical clustering and visualized the data as a graph where
nodes represented authors and edges represented cited by. The aim was to cluster the nodes into
6. International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.7, No.3, May 2017
50
groups sharing common characteristics; mutual information was applied to all clusters and
demonstrated strong links among the element of each cluster, except in cluster 5. Mutual
information conforms that cluster 5 elements share no links with the clusters and among
themselves no link was found between authors. Zero mutual information between two random
variables means that the variables are independent. As the discussion shows, mutual information
can provide a semantic interpretation of anomalous features.
5. SUMMARY
In this study, hierarchical clustering is applied to identify clusters and the data is visualised using
graph representation. Anomalies occur as a cluster among the data, such observations are
collective anomalies. Cluster validity with respect to anomalies can be difficult to evaluate
because of data volumes. This research has demonstrated that mutual information can be applied
to evaluate cluster content and the validity of the clustering approach. This also supports
validation of the visualisation element. This case study was developed to use mutual information
to validate the visualization graph. We used a real world data set where the anomalies were not
known in advance and the data required pre-processing. We were able to show that the approach
developed when scaled to large data volumes and combined with semantic pre-processing,
allowed us to work with noisy and inconsistent data. The co-citation data applied hierarchical
clustering and visualised the data as a graph where nodes represented authors and edges
represented cited-by. The aim was to cluster the nodes into groups sharing common
characteristics; mutual information was applied to all clusters and demonstrated strong links
among the element of each cluster, except in cluster 5. Mutual information conforms that cluster
5 elements share no links with the clusters and among themselves no link was found between
authors. Zero mutual information between two random variables means that the variables are
independent. Mutual information supported a semantic interpretation of the clusters, as shown by
the discussion of cluster 5. The experimental work confirmed the effectiveness and efficiency of
the proposed methods in practice.
In particular, this revealed that our method is able to deal with data sets with a large number of
objects and attributes. Having clustered and then visualised the data and examined the resulting
visualisation graph and the underlying cluster through mutual information, we were able to
determine that the results produced were valid, demonstrating that the approach can be used with
the real world data set. Anomalies detection finds applications in many domains, where it is
desirable to determine interesting and unusual events in the activity, which generates such data.
The core of all anomalies detection methods is the creation of a probabilistic, statistical or
algorithmic model, which characterises the normal behavior of the data. The deviations from this
model are used to determine the anomalies. A good domain-specific knowledge of the underlying
data is often crucial in order to design simple and accurate models, which do not over fit the
underlying data. Using mutual information contributes to our understanding of the anomalous
features and helps with semantic interpretation and to identify links with anomalous behavior.
The problem of anomalies detection becomes especially challenging, when significant
relationships exist among the different data points. This is the case for bibliographic data in
which the patterns in the relationships among the data points play a key role in defining the
anomalies. In the data used in this case study, there is significantly more complexity in terms of
how anomalies may be defined or modelled which can be used to interpret semantic meaning.
Therefore, anomalies may be defined in terms of significant changes in the underlying network
community or distance structure. Such models combine network analysis and change detection in
7. International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.7, No.3, May 2017
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order to detect structural and temporal anomalies from the underlying data. This research has
demonstrated that mutual information can be applied to evaluate cluster content and the validity
of the clustering approach. This also supports validation of the visualization element.
REFERENCES
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[3] IL-agure, Z. I. (2016). Anomalies in link mining based on mutual information). Staffordshire
University. UK.