This document discusses using data mining techniques like machine learning to analyze air quality data and generate models for predicting pollution levels. It summarizes applying decision trees and neural networks to data on pollutants and weather factors in Cambridge, UK. The models showed air temperature as the dominant predictor of ozone levels. While data mining provided insights, the author notes it is most useful complementing existing scientific domain knowledge and physical models of air quality.
Different Classification Technique for Data mining in Insurance Industry usin...IOSRjournaljce
this paper addresses the issues and techniques for Property/Casualty actuaries applying data mining methods. Data mining means the effective unknown pattern discovery from a large amount database. It is an interactive knowledge discovery procedure which is includes data acquisition, data integration, data exploration, model building, and model validation. The paper provides an overview of the data discovery method and introduces some important data mining method for application to insurance concluding cluster discovery approaches.
Evaluation of a Multiple Regression Model for Noisy and Missing Data IJECEIAES
The standard data collection problems may involve noiseless data while on the other hand large organizations commonly experience noisy and missing data, probably concerning data collected from individuals. As noisy and missing data will be significantly worrisome for occasions of the vast data collection then the investigation of different filtering techniques for big data environment would be remarkable. A multiple regression model where big data is employed for experimenting will be presented. Approximation for datasets with noisy and missing data is also proposed. The statistical root mean squared error (RMSE) associated with correlation coefficient (COEF) will be analyzed to prove the accuracy of estimators. Finally, results predicted by massive online analysis (MOA) will be compared to those real data collected from the following different time. These theoretical predictions with noisy and missing data estimation by simulation, revealing consistency with the real data are illustrated. Deletion mechanism (DEL) outperforms with the lowest average percentage of error.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
A CONCEPTUAL METADATA FRAMEWORK FOR SPATIAL DATA WAREHOUSEIJDKP
Metadata represents the information about data to be stored in Data Warehouses. It is a mandatory
element of Data Warehouse to build an efficient Data Warehouse. Metadata helps in data integration,
lineage, data quality and populating transformed data into data warehouse. Spatial data warehouses are
based on spatial data mostly collected from Geographical Information Systems (GIS) and the transactional
systems that are specific to an application or enterprise. Metadata design and deployment is the most
critical phase in building of data warehouse where it is mandatory to bring the spatial information and
data modeling together. In this paper, we present a holistic metadata framework that drives metadata
creation for spatial data warehouse. Theoretically, the proposed metadata framework improves the
efficiency of accessing of data in response to frequent queries on SDWs. In other words, the proposed
framework decreases the response time of the query and accurate information is fetched from Data
Warehouse including the spatial information
Different Classification Technique for Data mining in Insurance Industry usin...IOSRjournaljce
this paper addresses the issues and techniques for Property/Casualty actuaries applying data mining methods. Data mining means the effective unknown pattern discovery from a large amount database. It is an interactive knowledge discovery procedure which is includes data acquisition, data integration, data exploration, model building, and model validation. The paper provides an overview of the data discovery method and introduces some important data mining method for application to insurance concluding cluster discovery approaches.
Evaluation of a Multiple Regression Model for Noisy and Missing Data IJECEIAES
The standard data collection problems may involve noiseless data while on the other hand large organizations commonly experience noisy and missing data, probably concerning data collected from individuals. As noisy and missing data will be significantly worrisome for occasions of the vast data collection then the investigation of different filtering techniques for big data environment would be remarkable. A multiple regression model where big data is employed for experimenting will be presented. Approximation for datasets with noisy and missing data is also proposed. The statistical root mean squared error (RMSE) associated with correlation coefficient (COEF) will be analyzed to prove the accuracy of estimators. Finally, results predicted by massive online analysis (MOA) will be compared to those real data collected from the following different time. These theoretical predictions with noisy and missing data estimation by simulation, revealing consistency with the real data are illustrated. Deletion mechanism (DEL) outperforms with the lowest average percentage of error.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
A CONCEPTUAL METADATA FRAMEWORK FOR SPATIAL DATA WAREHOUSEIJDKP
Metadata represents the information about data to be stored in Data Warehouses. It is a mandatory
element of Data Warehouse to build an efficient Data Warehouse. Metadata helps in data integration,
lineage, data quality and populating transformed data into data warehouse. Spatial data warehouses are
based on spatial data mostly collected from Geographical Information Systems (GIS) and the transactional
systems that are specific to an application or enterprise. Metadata design and deployment is the most
critical phase in building of data warehouse where it is mandatory to bring the spatial information and
data modeling together. In this paper, we present a holistic metadata framework that drives metadata
creation for spatial data warehouse. Theoretically, the proposed metadata framework improves the
efficiency of accessing of data in response to frequent queries on SDWs. In other words, the proposed
framework decreases the response time of the query and accurate information is fetched from Data
Warehouse including the spatial information
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
Novel holistic architecture for analytical operation on sensory data relayed...IJECEIAES
With increasing adoption of the sensor-based application, there is an exponential rise of the sensory data that eventually take the shape of the big data. However, the practicality of executing high end analytical operation over the resource-constrained big data has never being studied closely. After reviewing existing approaches, it is explored that there is no cost-effective schemes of big data analytics over large scale sensory data processiing that can be directly used as a service. Therefore, the propsoed system introduces a holistic architecture where streamed data after performing extraction of knowedge can be offered in the form of services. Implemented in MATLAB, the proposed study uses a very simplistic approach considering energy constrained of the sensor nodes to find that proposed system offers better accuracy, reduced mining duration (i.e. faster response time), and reduced memory dependencies to prove that it offers cost effective analytical solution in contrast to existing system.
New proximity estimate for incremental update of non uniformly distributed cl...IJDKP
The conventional clustering algorithms mine static databases and generate a set of patterns in the form of
clusters. Many real life databases keep growing incrementally. For such dynamic databases, the patterns
extracted from the original database become obsolete. Thus the conventional clustering algorithms are not
suitable for incremental databases due to lack of capability to modify the clustering results in accordance
with recent updates. In this paper, the author proposes a new incremental clustering algorithm called
CFICA(Cluster Feature-Based Incremental Clustering Approach for numerical data) to handle numerical
data and suggests a new proximity metric called Inverse Proximity Estimate (IPE) which considers the
proximity of a data point to a cluster representative as well as its proximity to a farthest point in its vicinity.
CFICA makes use of the proposed proximity metric to determine the membership of a data point into a
cluster.
Ijricit 01-002 enhanced replica detection in short time for large data setsIjripublishers Ijri
Similarity check of real world entities is a necessary factor in these days which is named as Data Replica Detection.
Time is an critical factor today in tracking Data Replica Detection for large data sets, without having impact over quality
of Dataset. In this we primarily introduce two Data Replica Detection algorithms , where in these contribute enhanced
procedural standards in finding Data Replication at limited execution periods.This contribute better improvised state
of time than conventional techniques . We propose two Data Replica Detection algorithms namely progressive sorted
neighborhood method (PSNM), which performs best on small and almost clean datasets, and progressive blocking (PB),
which performs best on large and very grimy datasets. Both enhance the efficiency of duplicate detection even on very
large datasets.
A Novel Integrated Framework to Ensure Better Data Quality in Big Data Analyt...IJECEIAES
With advent of Big Data Analytics, the healthcare system is increasingly adopting the analytical services that is ultimately found to generate massive load of highly unstructured data. We reviewed the existing system to find that there are lesser number of solutions towards addressing the problems of data variety, data uncertainty, and data speed. It is important that an errorfree data should arrive in analytics. Existing system offers single-hand solution towards single platform. Therefore, we introduced an integrated framework that has the capability to address all these three problems in one execution time. Considering the synthetic big data of healthcare, we carried out the investigation to find that our proposed system using deep learning architecture offers better optimization of computational resources. The study outcome is found to offer comparatively better response time and higher accuracy rate as compared to existing optimization technqiues that is found and practiced widely in literature.
Anonymization of data using mapreduce on cloudeSAT Journals
Abstract In computer world cloud services are provided by the service providers. The user wants to share the private data which are stored in cloud server for different reasons like data mining, data analysis etc. These can bring the privacy concern. Privacy preservation can be satisfied by Anonymizing data sets through generalization to satisfy privacy requirements by using k-anonymity technique which is a widely used type of privacy preserving techniques. At present days the data of cloud applications are increasing their scale day by day concern with Big Data trend. So it is very difficult thing to accept, manage, maintain and process the large scaled data with-in the required time stamps. Thus for privacy preserving on privacy sensitive , large scaled data is very difficult task for existing anonymization techniques because they will not manage the scaled data sets. This approach addresses the anonymization problem on large scale cloud data sets using two phase top down specialization approach and MapReduce framework. Innovative MapReduce jobs are carefully designed in both phases of this technique to achieve specialization computation on scalable data sets. Scalability and efficiency of Top Down Specialization (TDS) is significantly increased over the existing approach. Keywords: Top Down Specialization, MapReduce, Data Anonymization, Cloud Computing, Privacy Preservation
An Analysis of Outlier Detection through clustering methodIJAEMSJORNAL
This research paper deals with an outlier which is known as an unusual behavior of any substance present in the spot. This is a detection process that can be employed for both anomaly detection and abnormal observation. This can be obtained through other members who belong to that data set. The deviation present in the outlier process can be attained by measuring certain terms like range, size, activity, etc. By detecting outlier one can easily reject the negativity present in the field. For instance, in healthcare, the health condition of a person can be determined through his latest health report or his regular activity. When found the person being inactive there may be a chance for that person to be sick. Many approaches have been used in this research paper for detecting outliers. The approaches used in this research are 1) Centroid based approach based on K-Means and Hierarchical Clustering algorithm and 2) through Clustering based approach. This approach may help in detecting outlier by grouping all similar elements in the same group. For grouping, the elements clustering method paves a way for it. This research paper will be based on the above mentioned 2 approaches.
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.
Drug Repurposing using Deep Learning on Knowledge GraphsDatabricks
Discovering new drugs is a lengthy and expensive process. This means that finding new uses for existing drugs can help create new treatments in less time and with less time. The difficulty is in finding these potential new uses.
How do we find these undiscovered uses for existing drugs?
We can unify the available structured and unstructured data sets into a knowledge graph. This is done by fusing the structured data sets, and performing named entity extraction on the unstructured data sets. Once this is done, we can use deep learning techniques to predict latent relationships.
In this talk we will cover:
Building the knowledge graph
Predicting latent relationships
Using the latent relationships to repurpose existing drugs
An Overview of Information Extraction from Mobile Wireless Sensor NetworksM H
Information Extraction (IE) is a key research area within the field of Wireless Sensor Networks (WSNs). It has been characterised in a variety of ways, ranging from the description of its purposes, to reasonably abstract models of its processes and components. There has been only a handful of papers addressing IE over mobile WSNs directly, these dealt with individual mobility related problems as the need arises. This paper is presented as a tutorial that takes the reader from the point of identifying data about a dynamic (mobile) real world problem, relating the data back to the world from which it was collected, and finally discovering what is in the data. It covers the entire process with special emphasis on how to exploit mobility in maximising information return from a mobile WSN. We present some challenges introduced by mobility on the IE process as well as its effects on the quality of the extracted information. Finally, we identify future research directions facing the development of efficient IE approaches for WSNs in the presence of mobility.
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
Novel holistic architecture for analytical operation on sensory data relayed...IJECEIAES
With increasing adoption of the sensor-based application, there is an exponential rise of the sensory data that eventually take the shape of the big data. However, the practicality of executing high end analytical operation over the resource-constrained big data has never being studied closely. After reviewing existing approaches, it is explored that there is no cost-effective schemes of big data analytics over large scale sensory data processiing that can be directly used as a service. Therefore, the propsoed system introduces a holistic architecture where streamed data after performing extraction of knowedge can be offered in the form of services. Implemented in MATLAB, the proposed study uses a very simplistic approach considering energy constrained of the sensor nodes to find that proposed system offers better accuracy, reduced mining duration (i.e. faster response time), and reduced memory dependencies to prove that it offers cost effective analytical solution in contrast to existing system.
New proximity estimate for incremental update of non uniformly distributed cl...IJDKP
The conventional clustering algorithms mine static databases and generate a set of patterns in the form of
clusters. Many real life databases keep growing incrementally. For such dynamic databases, the patterns
extracted from the original database become obsolete. Thus the conventional clustering algorithms are not
suitable for incremental databases due to lack of capability to modify the clustering results in accordance
with recent updates. In this paper, the author proposes a new incremental clustering algorithm called
CFICA(Cluster Feature-Based Incremental Clustering Approach for numerical data) to handle numerical
data and suggests a new proximity metric called Inverse Proximity Estimate (IPE) which considers the
proximity of a data point to a cluster representative as well as its proximity to a farthest point in its vicinity.
CFICA makes use of the proposed proximity metric to determine the membership of a data point into a
cluster.
Ijricit 01-002 enhanced replica detection in short time for large data setsIjripublishers Ijri
Similarity check of real world entities is a necessary factor in these days which is named as Data Replica Detection.
Time is an critical factor today in tracking Data Replica Detection for large data sets, without having impact over quality
of Dataset. In this we primarily introduce two Data Replica Detection algorithms , where in these contribute enhanced
procedural standards in finding Data Replication at limited execution periods.This contribute better improvised state
of time than conventional techniques . We propose two Data Replica Detection algorithms namely progressive sorted
neighborhood method (PSNM), which performs best on small and almost clean datasets, and progressive blocking (PB),
which performs best on large and very grimy datasets. Both enhance the efficiency of duplicate detection even on very
large datasets.
A Novel Integrated Framework to Ensure Better Data Quality in Big Data Analyt...IJECEIAES
With advent of Big Data Analytics, the healthcare system is increasingly adopting the analytical services that is ultimately found to generate massive load of highly unstructured data. We reviewed the existing system to find that there are lesser number of solutions towards addressing the problems of data variety, data uncertainty, and data speed. It is important that an errorfree data should arrive in analytics. Existing system offers single-hand solution towards single platform. Therefore, we introduced an integrated framework that has the capability to address all these three problems in one execution time. Considering the synthetic big data of healthcare, we carried out the investigation to find that our proposed system using deep learning architecture offers better optimization of computational resources. The study outcome is found to offer comparatively better response time and higher accuracy rate as compared to existing optimization technqiues that is found and practiced widely in literature.
Anonymization of data using mapreduce on cloudeSAT Journals
Abstract In computer world cloud services are provided by the service providers. The user wants to share the private data which are stored in cloud server for different reasons like data mining, data analysis etc. These can bring the privacy concern. Privacy preservation can be satisfied by Anonymizing data sets through generalization to satisfy privacy requirements by using k-anonymity technique which is a widely used type of privacy preserving techniques. At present days the data of cloud applications are increasing their scale day by day concern with Big Data trend. So it is very difficult thing to accept, manage, maintain and process the large scaled data with-in the required time stamps. Thus for privacy preserving on privacy sensitive , large scaled data is very difficult task for existing anonymization techniques because they will not manage the scaled data sets. This approach addresses the anonymization problem on large scale cloud data sets using two phase top down specialization approach and MapReduce framework. Innovative MapReduce jobs are carefully designed in both phases of this technique to achieve specialization computation on scalable data sets. Scalability and efficiency of Top Down Specialization (TDS) is significantly increased over the existing approach. Keywords: Top Down Specialization, MapReduce, Data Anonymization, Cloud Computing, Privacy Preservation
An Analysis of Outlier Detection through clustering methodIJAEMSJORNAL
This research paper deals with an outlier which is known as an unusual behavior of any substance present in the spot. This is a detection process that can be employed for both anomaly detection and abnormal observation. This can be obtained through other members who belong to that data set. The deviation present in the outlier process can be attained by measuring certain terms like range, size, activity, etc. By detecting outlier one can easily reject the negativity present in the field. For instance, in healthcare, the health condition of a person can be determined through his latest health report or his regular activity. When found the person being inactive there may be a chance for that person to be sick. Many approaches have been used in this research paper for detecting outliers. The approaches used in this research are 1) Centroid based approach based on K-Means and Hierarchical Clustering algorithm and 2) through Clustering based approach. This approach may help in detecting outlier by grouping all similar elements in the same group. For grouping, the elements clustering method paves a way for it. This research paper will be based on the above mentioned 2 approaches.
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.
Drug Repurposing using Deep Learning on Knowledge GraphsDatabricks
Discovering new drugs is a lengthy and expensive process. This means that finding new uses for existing drugs can help create new treatments in less time and with less time. The difficulty is in finding these potential new uses.
How do we find these undiscovered uses for existing drugs?
We can unify the available structured and unstructured data sets into a knowledge graph. This is done by fusing the structured data sets, and performing named entity extraction on the unstructured data sets. Once this is done, we can use deep learning techniques to predict latent relationships.
In this talk we will cover:
Building the knowledge graph
Predicting latent relationships
Using the latent relationships to repurpose existing drugs
An Overview of Information Extraction from Mobile Wireless Sensor NetworksM H
Information Extraction (IE) is a key research area within the field of Wireless Sensor Networks (WSNs). It has been characterised in a variety of ways, ranging from the description of its purposes, to reasonably abstract models of its processes and components. There has been only a handful of papers addressing IE over mobile WSNs directly, these dealt with individual mobility related problems as the need arises. This paper is presented as a tutorial that takes the reader from the point of identifying data about a dynamic (mobile) real world problem, relating the data back to the world from which it was collected, and finally discovering what is in the data. It covers the entire process with special emphasis on how to exploit mobility in maximising information return from a mobile WSN. We present some challenges introduced by mobility on the IE process as well as its effects on the quality of the extracted information. Finally, we identify future research directions facing the development of efficient IE approaches for WSNs in the presence of mobility.
The Role of Social Media and Artificial Intelligence for Disaster ResponseMuhammad Imran
Keynote slides for ISCRAM 2016.
"Social Media platforms such as Twitter are invaluable sources of time-critical information. Information on social media communicated during emergencies convey timely and actionable information. For rapid crisis response, real-time insights are important for emergency responders. Although, many humanitarian organizations would like to use this information, however they struggle due a number of issues such as information overload, information vagueness, less credible and misinformation. In this talk, I will describe the role of social media and potential artificial intelligence computational techniques useful for humanitarian organizations and decision makers to make sense of social media data for rapid crisis response."
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.
Ontology Based PMSE with Manifold PreferenceIJCERT
International journal from http://www.ijcert.org
IJCERT Standard on-line Journal
ISSN(Online):2349-7084,(An ISO 9001:2008 Certified Journal)
iso nicir csir
IJCERT (ISSN 2349–7084 (Online)) is approved by National Science Library (NSL), National Institute of Science Communication And Information Resources (NISCAIR), Council of Scientific and Industrial Research, New Delhi, India.
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.
Introduction to feature subset selection methodIJSRD
Data Mining is a computational progression to ascertain patterns in hefty data sets. It has various important techniques and one of them is Classification which is receiving great attention recently in the database community. Classification technique can solve several problems in different fields like medicine, industry, business, science. PSO is based on social behaviour for optimization problem. Feature Selection (FS) is a solution that involves finding a subset of prominent features to improve predictive accuracy and to remove the redundant features. Rough Set Theory (RST) is a mathematical tool which deals with the uncertainty and vagueness of the decision systems.
A SURVEY ON DATA MINING IN STEEL INDUSTRIESIJCSES Journal
In Industrial environments, huge amount of data is being generated which in turn collected indatabase anddata warehouses from all involved areas such as planning, process design, materials, assembly, production, quality, process control, scheduling, fault detection,shutdown, customer relation management, and so on. Data Mining has become auseful tool for knowledge acquisition for industrial process of Iron and steel making. Due to the rapid growth in Data Mining, various industries started using data mining technology to search the hidden patterns, which might further be used to the system with the new knowledge which might design new models to enhance the production quality, productivity optimum cost and maintenance etc. The continuous improvement of all steel production process regarding the avoidance of quality deficiencies and the related improvement of production yield is an essential task of steel producer. Therefore, zero defect strategy is popular today and to maintain it several quality assurancetechniques areused. The present report explains the methods of data mining and describes its application in the industrial environment and especially, in the steel industry.
Applying K-Means Clustering Algorithm to Discover Knowledge from Insurance Da...theijes
Data mining works to extract information known in advance from the enormous quantities of data which can lead to knowledge. It provides information that helps to make good decisions. The effectiveness of data mining in access to knowledge to achieve the goal of which is the discovery of the hidden facts contained in databases and through the use of multiple technologies. Clustering is organizing data into clusters or groups such that they have high intra-cluster similarity and low inter cluster similarity. This paper deals with K-means clustering algorithm which collect a number of data based on the characteristics and attributes of this data, and process the Clustering by reducing the distances between the data center. This algorithm is applied using open source tool called WEKA, with the Insurance dataset as its input
Survey of the Euro Currency Fluctuation by Using Data Miningijcsit
Data mining or Knowledge Discovery in Databases (KDD) is a new field in information technology that emerged because of progress in creation and maintenance of large databases by combining statistical and artificial intelligence methods with database management. Data mining is used to recognize hidden patterns and provide relevant information for decision making on complex problems where conventional methods are inecient or too slow. Data mining can be used as a powerful tool to predict future trends and behaviors, and this prediction allows making proactive, knowledge-driven decisions in businesses. Since the automated prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools, it can answer the business questions which are traditionally time consuming to resolve. Based on this great advantage, it provides more interest for the government, industry and commerce. In this paper we have used this tool to investigate the Euro currency fluctuation.For this investigation, we have three different algorithms: K*, IBK and MLP and we have extracted.Euro currency volatility by using the same criteria for all used algorithms. The used dataset has
21,084 records and is collected from daily price fluctuations in the Euro currency in the period
of10/2006 to 04/2010.
DATA SCIENCE METHODOLOGY FOR CYBERSECURITY PROJECTS cscpconf
Cybersecurity solutions are traditionally static and signature-based. The traditional solutions
along with the use of analytic models, machine learning and big data could be improved by
automatically trigger mitigation or provide relevant awareness to control or limit consequences
of threats. This kind of intelligent solutions is covered in the context of Data Science for
Cybersecurity. Data Science provides a significant role in cybersecurity by utilising the power
of data (and big data), high-performance computing and data mining (and machine learning) to
protect users against cybercrimes. For this purpose, a successful data science project requires
an effective methodology to cover all issues and provide adequate resources. In this paper, we
are introducing popular data science methodologies and will compare them in accordance with
cybersecurity challenges. A comparison discussion has also delivered to explain methodologies’
strengths and weaknesses in case of cybersecurity projects.
1. Data Mining and Science?
Knowledge discovery in science as opposed to business
Brian J Read
CLRC Rutherford Appleton Laboratory
Chilton, Didcot, Oxon OX11 0QX, UK
Brian.Read@rl.ac.uk
Keywords: Data Mining, Knowledge Discovery, Air Quality, Air Pollution
Abstract
The relatively new discipline of data mining is most often applied to extraction of useful
knowledge from business data. However, it is also useful in some scientific applications
where this more empirical approach complements traditional data analysis. The example of
machine learning from air quality data illustrates this alternative.
1. Introduction
Data Mining is the essential ingredient in the more general process of Knowledge Discovery
in Databases (KDD). The idea is that by automatically sifting through large quantities of data
it should be possible to extract nuggets of knowledge.
Data mining has become fashionable, not just in computer science (journals & conferences),
but particularly in business IT. (An example is its promotion by television advertising [1].)
The emergence is due to the growth in data warehouses and the realisation that this mass of
operational data has the potential to be exploited as an extension of Business Intelligence.
2. Why is Data Mining Different?
Data mining is more than just conventional data analysis. It uses traditional analysis tools
(like statistics and graphics) plus those associated with artificial intelligence (such as rule
induction and neural nets). It is all of these, but different. It is a distinctive approach or
attitude to data analysis. The emphasis is not so much on extracting facts, but on generating
hypotheses. The aim is more to yield questions rather than answers. Insights gained by data
mining can then be verified by conventional analysis.
3. The Data Management Context
“Information Technology” was originally “Data Processing”. Computing in the past gave
prominence to the processing algorithms - data were subservient. Typically, a program
processed input data tapes (such as master and detail records) in batch to output a new data
tape that incorporated the transactions. The structure of the data on the tapes reflected the
requirements of the specific algorithm. It was the era of Jackson Structured Programming.
The concept of database broke away from this algorithm-centric view. Data assumed an
existence independent of any programs. The data could be structured to reflect semantics of
relationships in the real world. One had successively hierarchical, network, relational and
1
2. object data models in commercial database management systems, each motivated by the
desire to model better the structure of actual entities and their relationships.
A database is extensional, storing many facts. Some information is intensional; that is, it
manifests as rules. Some limited success was achieved with deductive databases that stored
and manipulated rules, as for example in Prolog based systems. This encouraged Expert
Systems. However, it was hard to achieve solid success. A main difficulty was the
knowledge elicitation bottleneck: how to convert the thought processes of domain experts
into formal rules in a computer.
Data mining offers a solution: automatic rule extraction. By searching through large amounts
of data, one hopes to find sufficient instances of an association between data value
occurrences to suggest a statistically significant rule. However, a domain expert is still
needed to guide and evaluate the process and to apply the results.
4. Business Data Analysis
Popular commercial applications of data mining technology are, for example, in direct mail
targeting, credit scoring, churn prediction, stock trading, fraud detection, and customer
segmentation. It is closely allied to data warehousing in which large (gigabytes) corporate
databases are constructed for decision support applications. Rather than relational databases
with SQL, these are often multi-dimensional structures used for so-called on-line analytical
processing (OLAP). Data mining is a step further from the directed questioning and
reporting of OLAP in that the relevant results cannot be specified in advance.
5. Scientific Data Analysis
Rules generated by data mining are empirical - they are not physical laws. In most research
in the sciences, one compares recorded data with a theory that is founded on an analytic
expression of physical laws. The success or otherwise of the comparison is a test of the
hypothesis of how nature works expressed as a mathematical formula. This might be
something fundamental like an inverse square law. Alternatively, fitting a mathematical
model to the data might determine physical parameters (such as a refractive index).
On the other hand, where there are no general theories, data mining techniques are valuable,
especially where one has large quantities of data containing noisy patterns. This approach
hopes to obtain a theoretical generalisation automatically from the data by means of
induction, deriving empirical models and learning from examples. The resultant theory,
while maybe not fundamental, can yield a good understanding of the physical process and
can have great practical utility.
6. Scientific Applications
In a growing number of domains, the empirical or black box approach of data mining is good
science. Three typical examples are:
1. Sequence analysis in bioinformatics [2]
Genetic data such as the nucleotide sequences in genomic DNA are digital.
However, experimental data are inherently noisy, making the search for patterns and
the matching of sub-sequences difficult. Machine learning algorithms such as
artificial neural nets and hidden Markov chains are a very attractive way to tackle this
computationally demanding problem.
2. Classification of astronomical objects [3]
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3. The thousands of photographic plates that comprise a large survey of the night sky
contain around a billion faint objects. Having measured the attributes of each object,
the problem is to classify each object as a particular type of star or galaxy. Given the
number of features to consider, as well as the huge number of objects, decision-tree
learning algorithms have been found accurate and reliable for this task.
3. Medical decision support [4]
Patient records collected for diagnosis and prognosis include symptoms, bodily
measurements and laboratory test results. Machine learning methods have been applied
to a variety of medical domains to improve decision-making. Examples are the induction
of rules for early diagnosis of rheumatic diseases and neural nets to recognise the
clustered micro-calcifications in digitised mammograms that can lead to cancer.
The common technique is the use of data instances or cases to generate an empirical
algorithm that makes sense to the scientist and that can be put to practical use for recognition
or prediction.
7. Example of Predicting Air Quality
To illustrate the data mining approach, both advantages and disadvantages, this section
describes its application to a prediction of urban air pollution.
7.1 Motivation
One needs an understanding of the behaviour of air pollution in order to predict it and then to
guide any action to ameliorate it. Calculations with dynamical models are based on the
relevant physics and chemistry.
An interesting research and development project pursuing this approach is DECAIR [5]. This
concerns a generic system for exploiting existing urban air quality models by incorporating
land use and cloud cover data from remote sensing satellite images.
To help with the design and validation of such models, a complementary approach is
described here. It examines data on air quality empirically. Data mining and, in particular,
machine learning techniques are employed with two main objectives:
1. to improve our understanding of the relevant factors and their relationships, including the
possible discovery of non-obvious features in the data that may suggest better
formulations of the physical models;
2. to induce models solely from the data so that dynamical simulations might be compared to
them and that they may also have utility, offering (short term) predictive power.
7.2 Source Data
The investigation uses urban air quality measurements from the City of Cambridge (UK) [6].
These are especially useful since contemporary weather data from the same location are also
available. The objectives are, for example, to look for and interpret possible correlations
between each pollutant (NO, NO2, NOx, CO, O3 and PM10 particulates) and
a) the other pollutants;
b) the weather (wind strength and direction, temperature, relative humidity and radiance);
looking in particular for lags - that is, one attribute seeming to affect another with a delay of
perhaps hours or of days.
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4. 7.3 Data Preparation
Before trying to apply machine learning and constructing a model, there are three quite
important stages of data preparation. The data need to be cleaned, explored and transformed.
In typical applications, this can be most of the overall effort involved.
a) Cleaning
Though not elaborated here, but commonly a major part of the KDD process is data
cleaning. In this case, one is concerned with imposing consistent formats for dates and
times, allowing for missing data, finding duplicated data, and weeding out bad data - the
latter are not always obvious. The treatment of missing or erroneous data needs
application dependent judgement.
b) Exploration
Another major preliminary stage is a thorough examination of the data to acquire
familiarity and understanding. One starts with basic statistics - means, distributions,
ranges, etc - aiming to acquire a feeling for data quality. Other techniques such as
sorting, database queries and especially exploratory graphics help one gain confidence
with the data.
c) Transformation
The third preparatory step is dataset sampling, summarisation, transformation and
simplification. Working with only a sample of the full data, or applying a level of
aggregation, may well yield insights and results that are quicker (if not even discernible at
all) than with the complete data source. In addition, transforming the data by defining new
variables to work with can be a crucial step. Thus one might, for instance, calculate ratios of
observations, normalise them, or partition them into bins, bands or classes.
7.4 Initial Analysis
The initial analysis concentrated on the daily averages for the weather measurements and
daily maxima of the pollutants. This simplifies the problem, the results providing a guide for
a later full analysis. In addition, the peak values were further expressed as bands (e.g. “low”,
“medium” and “high”). For example, ozone (O3) values were encoded as
LOW <50 ppb
MEDIUM 50-90 ppb
HIGH >90 ppb
The bands relate to standards or targets set by the UK Expert Panel on Air Quality Standards
(EPAQS) that the public can appreciate. (For ozone, the recommended limit is 50 ppb as an
8 hour running average.)
The data exploration and analysis is guided by domain knowledge and enhanced by it.
Examination of the Cambridge air pollution data confirmed initial expectations:
• There is a daily cycle with peaks in the afternoon.
• Sundays have low pollution.
• An east wind (from industrial Europe) increases ozone levels.
• Sunlight on nitrogen dioxide (NO2) produces ozone.
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5. • Particulates (PM10) come from vehicle exhausts.
Cambridge has little industry and within an urban environment traffic is the dominant
pollution agent. Its effect depends on the local street topography so mesoscale dynamical
models have restricted value.
7.5 Modelling
The two principal machine learning techniques used in this application are neural networks
and the induction of decision trees. Expressing their predictions as band values makes the
results of such models easier to understand.
a) Decision Trees
Applying the C5.0 algorithm to the data to generate a simple decision tree, one gets for ozone
bands:
AirTemp =< 28.3 -> LOW
AirTemp > 28.3
RelHum =< 58.1 -> HIGH
RelHum > 58.1 -> MEDIUM
This suggests how the ozone concentration depends mainly on the air temperature and
relative humidity. The same tree, expressed as a rule set is:
Rules for HIGH:
Rule #1 for HIGH:
if AirTemp > 28.3
and RelHum =< 58.1
then -> HIGH
Rules for LOW:
Rule #1 for LOW:
if AirTemp =< 28.3
then -> LOW
Rules for MEDIUM:
Rule #1 for MEDIUM:
if AirTemp > 28.3
and RelHum > 58.1
then -> MEDIUM
Default : -> LOW
In fact, the support for these rules is modest. The handicap is that there are too few instances
of HIGH ozone days in the data. Reliable predictions would need something more elaborate,
but this illustrates the idea.
b) Neural Networks
Alternatively, the daily data can be fitted with an artificial neural network to model the ozone
band value. A first attempt yields:
Neural Network "O3band" architecture
Input Layer : 5 neurons
Hidden Layer #1 : 4 neurons
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6. Output Layer : 4 neurons
Predicted Accuracy : 96%
Relative Importance of Inputs
AirTemp : 0.29
RelHum : 0.06
Rad : 0.04
Wet : 0.02
WindSpeed : 0.004
Again, this shows that air temperature is the dominant predictor. However, given the limited
quantity of data summarised to daily values, it is not worth trying to refine the model
network.
7.6 Software
The air quality data were analysed using the data mining software package Clementine [7]
(originally from Integral Solutions Ltd. and now from SPSS Inc.) While this provides
standard machine learning algorithms to generate models, its great virtue is the powerful
visual environment it offers for data exploration. The figure shows an example of a data
processing stream in Clementine’s graphical interface. This ease of data exploration and
modelling is crucial in allowing the domain expert to attack the problem and find applicable
results.
7.7 Conclusions
Work so far supports the common experience in data mining that most of the effort is in data
preparation and exploration. The data must be cleaned to allow for missing and bad
measurements. Detailed examination leads to transforming the data into more effective
forms. The modelling process is very iterative, using statistics and visualisation to guide
strategy. The temporal dimension with its lagged correlations adds significantly to the search
space for the most relevant parameters.
Investigation that is more extensive is needed to establish under what circumstances data
mining might be as effective as dynamical modelling. (For instance, urban air quality varies
greatly from street to street depending on buildings and traffic.) A feature of data mining is
that it can “short circuit” the post-interpretation of the output of numerical simulations by
directly predicting the probability of exceeding pollution thresholds. A drawback is the need
for large datasets in order to provide enough high pollution episodes for reliable rule
induction. More generally, data mining analysis is useful to provide a reference model in the
validation of physically based simulation calculations.
8. Summary
Is data mining as useful in science as in commerce? Certainly, data mining in science has
much in common with that for business data. One difference, though, is that there is a lot of
existing scientific theory and knowledge. Hence, there is less chance of knowledge emerging
purely from data. However, empirical results can be valuable in science (especially where it
borders on engineering) as in suggesting causality relationships or for modelling complex
phenomena.
Another difference is that in commerce, rules are soft - sociological or cultural – and assume
consistent behaviour. For example, the plausible myth that “30% of people who buy babies’
nappies also buy beer” is hardly fundamental, but one might profitably apply it as a selling
tactic (until perhaps the fashion changes from beer to lager).
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7. On the other hand, scientific rules or laws are, in principle, testable objectively. Any results
from data mining techniques must sit within the existing domain knowledge. Hence, the
involvement of a domain expert is crucial to the data mining process.
Naïve data mining often yields “obvious” results. The challenge is to incorporate rules
known a priori into the empirical induction, remembering that the whole KDD process is
exploratory and iterative.
References
[1] http://www.ibm.com/sfasp/locations/milan/index.html
[2] P Baldi and S Brunak, Bioinformatics - The Machine Learning Approach, MIT Press,
1998.
[3] U M Fayyad, S G Djorgovski and N Weir, Automating the Analysis and Cataloging of
Sky Surveys, in U M Fayyad et al (eds.), Advances in Knowledge Discovery and Data
Mining, p471, AAIT Press and MIT Press, 1996.
[4] N Lavrač, E Keravnou and B Zupan (eds.), Intelligent Data Analysis in Medicine and
Pharmacology, Kluwer, 1997
[5] Development of an earth observation data converter with application to air quality
forecast (DECAIR): http://www-air.inria.fr/decair/
[6] Cambridge City Council Air Quality Monitor:
http://www.io-ltd.co.uk/ccc.html
[7] http://www.spss.com/software/clementine/ and
http://www.isl.co.uk/
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8. The Clementine user interface: The two source data files of weather and air pollutants
measurements are merged, ozone bands selected and models (C5 decision tree and neural net)
generated.
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