The document discusses data mining, including its definition, main operations, techniques, applications, and relationship to data warehousing. The four main data mining operations are predictive modeling, database segmentation, link analysis, and deviation detection. Each operation uses specific techniques like classification, clustering, association rule mining, and visualization. Data mining is applied in domains like retail, banking, insurance, and medicine. It works best with large, clean datasets typically stored in data warehouses.
This document discusses various data mining techniques, including artificial neural networks. It provides an overview of the knowledge discovery in databases process and the cross-industry standard process for data mining. It also describes techniques such as classification, clustering, regression, association rules, and neural networks. Specifically, it discusses how neural networks are inspired by biological neural networks and can be used to model complex relationships in data.
Additional themes of data mining for Msc CSThanveen
Data mining involves using computational techniques from machine learning, statistics, and database systems to discover patterns in large data sets. There are several theoretical foundations of data mining including data reduction, data compression, pattern discovery, probability theory, and inductive databases. Statistical techniques like regression, generalized linear models, analysis of variance, and time series analysis are also used for statistical data mining. Visual data mining integrates data visualization techniques with data mining to discover implicit knowledge. Audio data mining uses audio signals to represent data mining patterns and results. Collaborative filtering is commonly used for product recommendations based on opinions of other customers. Privacy and security of personal data are important social concerns of data mining.
A simulated decision trees algorithm (sdt)Mona Nasr
The customer's information contained in
databases has increased dramatically in the last few years.
Data mining is a good approach to deal with this volume of
information to enhance the process of customer services.
One of the most important and powerful techniques of data
mining is decision trees algorithm. It appropriate for large
and sophisticated business area but it's complicated, high
cost and not easy to use by not specialists in the field. To
overcome this problem SDT is proposed which is a simple,
powerful and low-cost proposed methodology to simulate the
decision trees algorithm for different business scopes and
nature. SDT methodology consists of three phases. The first
phase is the data preparation which prepare data for
computing calculations, the second phase is SDT algorithm
which represents a simulation of decision trees algorithm to
find the most important rules that distinguish specific type of
customers, the third phase is to visualize results and rules for
better understanding and clarifying the results. In this paper
SDT methodology is tested by a dataset consists of 1000
instants for German Credit Data belongs to one of German
bank customers. SDT selects the most important rules and
paths that reaches the selected ratio and tested cluster of
customers successfully with interesting remarks and finding.
A Survey on the Clustering Algorithms in Sales Data MiningEditor IJCATR
This document discusses clustering techniques that can be used for analyzing sales data. It begins by introducing the importance of clustering large sales databases to extract useful knowledge that can help senior management with decision making. It then provides an overview of different clustering algorithms like hierarchical, partitioning, grid-based, and density-based clustering. The document also discusses the goals of clustering sales data, which include predicting customer purchasing behavior and improving knowledge discovery. It outlines the typical stages of sales data clustering as feature selection, validation of results, and interpretation of results. Finally, it reviews several papers that have used clustering and other techniques like association rule mining to analyze retail sales data.
Study of Data Mining Methods and its ApplicationsIRJET Journal
This document discusses data mining methods and their applications. It begins by defining data mining as the process of extracting useful patterns from large amounts of data. The document then outlines the typical steps in the knowledge discovery process, including data selection, preprocessing, transformation, mining, and evaluation. It classifies data mining techniques into predictive and descriptive methods. Specific techniques discussed include classification, clustering, prediction, and association rule mining. Finally, the document discusses applications of data mining in fields like healthcare, biology, retail, and banking.
PERFORMING DATA MINING IN (SRMS) THROUGH VERTICAL APPROACH WITH ASSOCIATION R...Editor IJMTER
This system technique is used for efficient data mining in SRMS (Student Records
Management System) through vertical approach with association rules in distributed databases. The
current leading technique is that of Kantarcioglu and Clifton[1]. In this system I deal with two
challenges or issues, one that computes the union of private subsets that each of the interacting users
hold, and another that tests the inclusion of an element held by one user in a subset held by another.
The existing system uses different techniques for data mining purpose like Apriori algorithm. The
Fast Distributed Mining (FDM) algorithm of Cheung et al. [2], which is an unsecured distributed
version of the Apriori algorithm. Proposed system offers enhanced privacy and data mining with
respect to the Encryption techniques and Association rule with Fp-Growth Algorithm in private
cloud (system contains different files of subjects with respect to their branches). Due to this above
techniques the expected effect on this system is that, it is simpler and more efficient in terms of
communication cost and combinational cost. Due to these techniques it will affect the parameter like
time consumption for execution, length of the code is decrease, find the data fast, extracting hidden
predictive information from large databases and the efficiency of this proposed system should
increase by the 20%.
Data mining involves finding hidden patterns in large datasets. It differs from traditional data access in that the query may be unclear, the data has been preprocessed, and the output is an analysis rather than a data subset. Data mining algorithms attempt to fit models to the data by examining attributes, criteria for preference of one model over others, and search techniques. Common data mining tasks include classification, regression, clustering, association rule learning, and prediction.
This document discusses various data mining techniques, including artificial neural networks. It provides an overview of the knowledge discovery in databases process and the cross-industry standard process for data mining. It also describes techniques such as classification, clustering, regression, association rules, and neural networks. Specifically, it discusses how neural networks are inspired by biological neural networks and can be used to model complex relationships in data.
Additional themes of data mining for Msc CSThanveen
Data mining involves using computational techniques from machine learning, statistics, and database systems to discover patterns in large data sets. There are several theoretical foundations of data mining including data reduction, data compression, pattern discovery, probability theory, and inductive databases. Statistical techniques like regression, generalized linear models, analysis of variance, and time series analysis are also used for statistical data mining. Visual data mining integrates data visualization techniques with data mining to discover implicit knowledge. Audio data mining uses audio signals to represent data mining patterns and results. Collaborative filtering is commonly used for product recommendations based on opinions of other customers. Privacy and security of personal data are important social concerns of data mining.
A simulated decision trees algorithm (sdt)Mona Nasr
The customer's information contained in
databases has increased dramatically in the last few years.
Data mining is a good approach to deal with this volume of
information to enhance the process of customer services.
One of the most important and powerful techniques of data
mining is decision trees algorithm. It appropriate for large
and sophisticated business area but it's complicated, high
cost and not easy to use by not specialists in the field. To
overcome this problem SDT is proposed which is a simple,
powerful and low-cost proposed methodology to simulate the
decision trees algorithm for different business scopes and
nature. SDT methodology consists of three phases. The first
phase is the data preparation which prepare data for
computing calculations, the second phase is SDT algorithm
which represents a simulation of decision trees algorithm to
find the most important rules that distinguish specific type of
customers, the third phase is to visualize results and rules for
better understanding and clarifying the results. In this paper
SDT methodology is tested by a dataset consists of 1000
instants for German Credit Data belongs to one of German
bank customers. SDT selects the most important rules and
paths that reaches the selected ratio and tested cluster of
customers successfully with interesting remarks and finding.
A Survey on the Clustering Algorithms in Sales Data MiningEditor IJCATR
This document discusses clustering techniques that can be used for analyzing sales data. It begins by introducing the importance of clustering large sales databases to extract useful knowledge that can help senior management with decision making. It then provides an overview of different clustering algorithms like hierarchical, partitioning, grid-based, and density-based clustering. The document also discusses the goals of clustering sales data, which include predicting customer purchasing behavior and improving knowledge discovery. It outlines the typical stages of sales data clustering as feature selection, validation of results, and interpretation of results. Finally, it reviews several papers that have used clustering and other techniques like association rule mining to analyze retail sales data.
Study of Data Mining Methods and its ApplicationsIRJET Journal
This document discusses data mining methods and their applications. It begins by defining data mining as the process of extracting useful patterns from large amounts of data. The document then outlines the typical steps in the knowledge discovery process, including data selection, preprocessing, transformation, mining, and evaluation. It classifies data mining techniques into predictive and descriptive methods. Specific techniques discussed include classification, clustering, prediction, and association rule mining. Finally, the document discusses applications of data mining in fields like healthcare, biology, retail, and banking.
PERFORMING DATA MINING IN (SRMS) THROUGH VERTICAL APPROACH WITH ASSOCIATION R...Editor IJMTER
This system technique is used for efficient data mining in SRMS (Student Records
Management System) through vertical approach with association rules in distributed databases. The
current leading technique is that of Kantarcioglu and Clifton[1]. In this system I deal with two
challenges or issues, one that computes the union of private subsets that each of the interacting users
hold, and another that tests the inclusion of an element held by one user in a subset held by another.
The existing system uses different techniques for data mining purpose like Apriori algorithm. The
Fast Distributed Mining (FDM) algorithm of Cheung et al. [2], which is an unsecured distributed
version of the Apriori algorithm. Proposed system offers enhanced privacy and data mining with
respect to the Encryption techniques and Association rule with Fp-Growth Algorithm in private
cloud (system contains different files of subjects with respect to their branches). Due to this above
techniques the expected effect on this system is that, it is simpler and more efficient in terms of
communication cost and combinational cost. Due to these techniques it will affect the parameter like
time consumption for execution, length of the code is decrease, find the data fast, extracting hidden
predictive information from large databases and the efficiency of this proposed system should
increase by the 20%.
Data mining involves finding hidden patterns in large datasets. It differs from traditional data access in that the query may be unclear, the data has been preprocessed, and the output is an analysis rather than a data subset. Data mining algorithms attempt to fit models to the data by examining attributes, criteria for preference of one model over others, and search techniques. Common data mining tasks include classification, regression, clustering, association rule learning, and prediction.
Data mining refers to extracting hidden patterns from large databases and is a step in the Knowledge Discovery in Databases (KDD) process. KDD is the broader process of finding knowledge within data and involves data preparation, pattern analysis, and knowledge evaluation. It is needed due to the impracticality of manually analyzing large, complex databases. The KDD process includes understanding goals, data selection, preprocessing, mining, pattern recognition, interpretation, and discovery. Examples of applying KDD include grouping students, predicting enrollments, and assessing student performance.
The document provides an introduction to data mining and knowledge discovery. It discusses how large amounts of data are extracted and transformed into useful information for applications like market analysis and fraud detection. The key steps in the knowledge discovery process are described as data cleaning, integration, selection, transformation, mining, pattern evaluation, and knowledge presentation. Common data sources, database architectures, and types of coupling between data mining systems and databases are also outlined.
This document provides a literature review on data mining with Oracle 10g using clustering and classification algorithms. It discusses the data mining process and common algorithms used, including Naive Bayes, decision trees, k-means clustering, and neural networks. The review categorizes data mining techniques into supervised learning (classification, prediction) and unsupervised learning (clustering, association rule mining). It also outlines the typical 4-step data mining process of problem definition, data preparation, model building and evaluation, and knowledge deployment.
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.
Shivani Soni presented on data mining. Data mining involves using computational methods to discover patterns in large datasets, combining techniques from machine learning, statistics, artificial intelligence, and database systems. It is used to extract useful information from data and transform it into an understandable structure. Data mining has various applications, including in sales/marketing, banking/finance, healthcare/insurance, transportation, medicine, education, manufacturing, and research analysis. It enables businesses to understand customer purchasing patterns and maximize profits. Examples of its use include fraud detection, credit risk analysis, stock trading, customer loyalty analysis, distribution scheduling, claims analysis, risk profiling, detecting medical therapy patterns, education decision making, and aiding manufacturing process design and research.
BUILDING A GENERAL CONCEPT OF ANALYTICAL SERVICES FOR ANALYSIS OF STRUCTURED ...Kiogyf
BUILDING A GENERAL CONCEPT OF ANALYTICAL SERVICES FOR ANALYSIS OF STRUCTURED DATA
Abstract:
In this paper, “Building a common concept of analytical services for analyzing structured data” was proposed to build an analytical service to provide forecasts, descriptive and comparative data summaries using modern Microsoft technologies. This service will allow users to pehttps://www.slideshare.net/uploadrform flexible viewing of information, receive arbitrary data slices and perform analytical operations of drill-down, convolution, pass-through distribution, the comparison in time. With the help of data mining, it is possible to detect previously unknown, non-trivial, practically useful and accessible interpretations of knowledge that are necessary for the organization's decision-making. Also, each client can interact with the service and thus monitor the displayed analytical information. In the process of work the following tasks were solved: investigated the subject area; studied materials relating to systems and technologies for their implementation; designed service architecture and applications to configure the service; selected technologies and tools for the implementation of the system; implemented the main frame of the system; modules for interaction with analysis services, data mining (a priori algorithm) and partially a module of neural networks; a report was written and a presentation of the results was prepared; The developed service will be useful to all organizations that are interested in obtaining analytical reports and other previously unknown information on their accumulated data. For example, organizations can analyze the impact of advertising, customer segmentation, search for signs of profitable customers, analyze product preferences, forecast sales volumes, and more.
A new hybrid algorithm for business intelligence recommender systemIJNSA Journal
Business Intelligence is a set of methods, process and technologies that transform raw data into meaningful
and useful information. Recommender system is one of business intelligence system that is used to obtain
knowledge to the active user for better decision making. Recommender systems apply data mining
techniques to the problem of making personalized recommendations for information. Due to the growth in
the number of information and the users in recent years offers challenges in recommender systems.
Collaborative, content, demographic and knowledge-based are four different types of recommendations
systems. In this paper, a new hybrid algorithm is proposed for recommender system which combines
knowledge based, profile of the users and most frequent item mining technique to obtain intelligence.
Data mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner.
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
Dwdm chapter 5 data mining a closer lookShengyou Lin
This chapter discusses data mining strategies and techniques. It introduces classification, estimation, prediction, clustering, and market basket analysis as common strategies. Supervised techniques like decision trees, neural networks, and regression are covered. Unsupervised clustering and association rules are also discussed. The chapter concludes with an overview of evaluating model performance for both supervised and unsupervised learning.
The document discusses knowledge acquisition and data mining. It begins by defining knowledge acquisition as the process of discovering useful patterns or rules in large quantities of data through automatic or semi-automatic means. It then discusses why knowledge acquisition is important due to factors like data explosion and competitive pressure. The document also discusses different types of knowledge that can be mined, including classes, clusters, associations and sequential patterns. It outlines the predictive and descriptive approaches in data mining and common tasks like classification, clustering and association rule mining. Finally, it presents the typical steps in the knowledge discovery process including data selection, pre-processing, transformation, data mining, and interpretation.
Predicting the Credit Defaulter is a perilous task of Financial Industries like Banks. Ascertainingnon payer
before giving loan is a significant and conflict-ridden task of the Banker. Classification techniques
are the better choice for predictive analysis like finding the claimant, whether he/she is an unpretentious
customer or a cheat. Defining the outstanding classifier is a risky assignment for any industrialist like a
banker. This allow computer science researchers to drill down efficient research works through evaluating
different classifiers and finding out the best classifier for such predictive problems. This research
work investigates the productivity of LADTree Classifier and REPTree Classifier for the credit risk prediction
and compares their fitness through various measures. German credit dataset has been taken and used
to predict the credit risk with a help of open source machine learning tool.
The International Journal of Engineering and Sciencetheijes
This document summarizes a research paper on discovering actionable knowledge through multi-step data mining. The paper proposes a framework that combines multiple data sources, mining methods, and features to generate comprehensive patterns. This approach aims to provide more reliable and dependable intelligence than single-step mining. The framework integrates multi-source, multi-method, and multi-feature combined mining techniques. A prototype application demonstrated the effectiveness of the proposed combined mining approach for generating actionable knowledge from complex enterprise data.
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.
Multiple Minimum Support Implementations with Dynamic Matrix Apriori Algorith...ijsrd.com
Data mining can be defined as the process of uncovering hidden patterns in random data that are potentially useful. The discovery of interesting association relationships among large amounts of business transactions is currently vital for making appropriate business decisions. Association rule analysis is the task of discovering association rules that occur frequently in a given transaction data set. Its task is to find certain relationships among a set of data (itemset) in the database. It has two measurements: Support and confidence values. Confidence value is a measure of rule’s strength, while support value corresponds to statistical significance. There are currently a variety of algorithms to discover association rules. Some of these algorithms depend on the use of minimum support to weed out the uninteresting rules. Other algorithms look for highly correlated items, that is, rules with high confidence. Traditional association rule mining techniques employ predefined support and confidence values. However, specifying minimum support value of the mined rules in advance often leads to either too many or too few rules, which negatively impacts the performance of the overall system. This work proposes a way to efficiently mine association rules over dynamic databases using Dynamic Matrix Apriori technique and Multiple Support Apriori (MSApriori). A modification for Matrix Apriori algorithm to accommodate this modification is proposed. Experiments on large set of data bases have been conducted to validate the proposed framework. The achieved results show that there is a remarkable improvement in the overall performance of the system in terms of run time, the number of generated rules, and number of frequent items used.
This document discusses data mining and related topics. It begins by defining data mining as the process of discovering patterns in large datasets using methods from machine learning, statistics, and database systems. The document then discusses data warehouses, how they work, and their role in data mining. It describes different data mining functionalities and tasks such as classification, prediction, and clustering. The document outlines some common data mining applications and issues related to methodology, performance, and diverse data types. Finally, it discusses some social implications of data mining involving privacy, profiling, and unauthorized use of data.
This document provides an overview of key concepts in data mining including data preprocessing, data warehouses, frequent patterns, association rule mining, classification, clustering, outlier analysis and more. It discusses different types of databases that can be mined such as relational, transactional, temporal and spatial databases. The document also covers data characterization, discrimination, interestingness measures and different types of data mining systems.
Recommendation system using bloom filter in mapreduceIJDKP
Many clients like to use the Web to discover product details in the form of online reviews. The reviews are
provided by other clients and specialists. Recommender systems provide an important response to the
information overload problem as it presents users more practical and personalized information facilities.
Collaborative filtering methods are vital component in recommender systems as they generate high-quality
recommendations by influencing the likings of society of similar users. The collaborative filtering method
has assumption that people having same tastes choose the same items. The conventional collaborative
filtering system has drawbacks as sparse data problem & lack of scalability. A new recommender system is
required to deal with the sparse data problem & produce high quality recommendations in large scale
mobile environment. MapReduce is a programming model which is widely used for large-scale data
analysis. The described algorithm of recommendation mechanism for mobile commerce is user based
collaborative filtering using MapReduce which reduces scalability problem in conventional CF system.
One of the essential operations for the data analysis is join operation. But MapReduce is not very
competent to execute the join operation as it always uses all records in the datasets where only small
fraction of datasets are applicable for the join operation. This problem can be reduced by applying
bloomjoin algorithm. The bloom filters are constructed and used to filter out redundant intermediate
records. The proposed algorithm using bloom filter will reduce the number of intermediate results and will
improve the join performance.
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.
Data mining involves analyzing large amounts of data to discover patterns that can be used for purposes such as increasing sales, reducing costs, or detecting fraud. It allows companies to better understand customer behavior and develop more effective marketing strategies. Common data mining techniques used by retailers include loyalty programs to track purchasing patterns and target customers with personalized coupons. Data mining software uses techniques like classification, clustering, and prediction to analyze data from different perspectives and extract useful information and patterns.
Data mining refers to extracting hidden patterns from large databases and is a step in the Knowledge Discovery in Databases (KDD) process. KDD is the broader process of finding knowledge within data and involves data preparation, pattern analysis, and knowledge evaluation. It is needed due to the impracticality of manually analyzing large, complex databases. The KDD process includes understanding goals, data selection, preprocessing, mining, pattern recognition, interpretation, and discovery. Examples of applying KDD include grouping students, predicting enrollments, and assessing student performance.
The document provides an introduction to data mining and knowledge discovery. It discusses how large amounts of data are extracted and transformed into useful information for applications like market analysis and fraud detection. The key steps in the knowledge discovery process are described as data cleaning, integration, selection, transformation, mining, pattern evaluation, and knowledge presentation. Common data sources, database architectures, and types of coupling between data mining systems and databases are also outlined.
This document provides a literature review on data mining with Oracle 10g using clustering and classification algorithms. It discusses the data mining process and common algorithms used, including Naive Bayes, decision trees, k-means clustering, and neural networks. The review categorizes data mining techniques into supervised learning (classification, prediction) and unsupervised learning (clustering, association rule mining). It also outlines the typical 4-step data mining process of problem definition, data preparation, model building and evaluation, and knowledge deployment.
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.
Shivani Soni presented on data mining. Data mining involves using computational methods to discover patterns in large datasets, combining techniques from machine learning, statistics, artificial intelligence, and database systems. It is used to extract useful information from data and transform it into an understandable structure. Data mining has various applications, including in sales/marketing, banking/finance, healthcare/insurance, transportation, medicine, education, manufacturing, and research analysis. It enables businesses to understand customer purchasing patterns and maximize profits. Examples of its use include fraud detection, credit risk analysis, stock trading, customer loyalty analysis, distribution scheduling, claims analysis, risk profiling, detecting medical therapy patterns, education decision making, and aiding manufacturing process design and research.
BUILDING A GENERAL CONCEPT OF ANALYTICAL SERVICES FOR ANALYSIS OF STRUCTURED ...Kiogyf
BUILDING A GENERAL CONCEPT OF ANALYTICAL SERVICES FOR ANALYSIS OF STRUCTURED DATA
Abstract:
In this paper, “Building a common concept of analytical services for analyzing structured data” was proposed to build an analytical service to provide forecasts, descriptive and comparative data summaries using modern Microsoft technologies. This service will allow users to pehttps://www.slideshare.net/uploadrform flexible viewing of information, receive arbitrary data slices and perform analytical operations of drill-down, convolution, pass-through distribution, the comparison in time. With the help of data mining, it is possible to detect previously unknown, non-trivial, practically useful and accessible interpretations of knowledge that are necessary for the organization's decision-making. Also, each client can interact with the service and thus monitor the displayed analytical information. In the process of work the following tasks were solved: investigated the subject area; studied materials relating to systems and technologies for their implementation; designed service architecture and applications to configure the service; selected technologies and tools for the implementation of the system; implemented the main frame of the system; modules for interaction with analysis services, data mining (a priori algorithm) and partially a module of neural networks; a report was written and a presentation of the results was prepared; The developed service will be useful to all organizations that are interested in obtaining analytical reports and other previously unknown information on their accumulated data. For example, organizations can analyze the impact of advertising, customer segmentation, search for signs of profitable customers, analyze product preferences, forecast sales volumes, and more.
A new hybrid algorithm for business intelligence recommender systemIJNSA Journal
Business Intelligence is a set of methods, process and technologies that transform raw data into meaningful
and useful information. Recommender system is one of business intelligence system that is used to obtain
knowledge to the active user for better decision making. Recommender systems apply data mining
techniques to the problem of making personalized recommendations for information. Due to the growth in
the number of information and the users in recent years offers challenges in recommender systems.
Collaborative, content, demographic and knowledge-based are four different types of recommendations
systems. In this paper, a new hybrid algorithm is proposed for recommender system which combines
knowledge based, profile of the users and most frequent item mining technique to obtain intelligence.
Data mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner.
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
Dwdm chapter 5 data mining a closer lookShengyou Lin
This chapter discusses data mining strategies and techniques. It introduces classification, estimation, prediction, clustering, and market basket analysis as common strategies. Supervised techniques like decision trees, neural networks, and regression are covered. Unsupervised clustering and association rules are also discussed. The chapter concludes with an overview of evaluating model performance for both supervised and unsupervised learning.
The document discusses knowledge acquisition and data mining. It begins by defining knowledge acquisition as the process of discovering useful patterns or rules in large quantities of data through automatic or semi-automatic means. It then discusses why knowledge acquisition is important due to factors like data explosion and competitive pressure. The document also discusses different types of knowledge that can be mined, including classes, clusters, associations and sequential patterns. It outlines the predictive and descriptive approaches in data mining and common tasks like classification, clustering and association rule mining. Finally, it presents the typical steps in the knowledge discovery process including data selection, pre-processing, transformation, data mining, and interpretation.
Predicting the Credit Defaulter is a perilous task of Financial Industries like Banks. Ascertainingnon payer
before giving loan is a significant and conflict-ridden task of the Banker. Classification techniques
are the better choice for predictive analysis like finding the claimant, whether he/she is an unpretentious
customer or a cheat. Defining the outstanding classifier is a risky assignment for any industrialist like a
banker. This allow computer science researchers to drill down efficient research works through evaluating
different classifiers and finding out the best classifier for such predictive problems. This research
work investigates the productivity of LADTree Classifier and REPTree Classifier for the credit risk prediction
and compares their fitness through various measures. German credit dataset has been taken and used
to predict the credit risk with a help of open source machine learning tool.
The International Journal of Engineering and Sciencetheijes
This document summarizes a research paper on discovering actionable knowledge through multi-step data mining. The paper proposes a framework that combines multiple data sources, mining methods, and features to generate comprehensive patterns. This approach aims to provide more reliable and dependable intelligence than single-step mining. The framework integrates multi-source, multi-method, and multi-feature combined mining techniques. A prototype application demonstrated the effectiveness of the proposed combined mining approach for generating actionable knowledge from complex enterprise data.
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.
Multiple Minimum Support Implementations with Dynamic Matrix Apriori Algorith...ijsrd.com
Data mining can be defined as the process of uncovering hidden patterns in random data that are potentially useful. The discovery of interesting association relationships among large amounts of business transactions is currently vital for making appropriate business decisions. Association rule analysis is the task of discovering association rules that occur frequently in a given transaction data set. Its task is to find certain relationships among a set of data (itemset) in the database. It has two measurements: Support and confidence values. Confidence value is a measure of rule’s strength, while support value corresponds to statistical significance. There are currently a variety of algorithms to discover association rules. Some of these algorithms depend on the use of minimum support to weed out the uninteresting rules. Other algorithms look for highly correlated items, that is, rules with high confidence. Traditional association rule mining techniques employ predefined support and confidence values. However, specifying minimum support value of the mined rules in advance often leads to either too many or too few rules, which negatively impacts the performance of the overall system. This work proposes a way to efficiently mine association rules over dynamic databases using Dynamic Matrix Apriori technique and Multiple Support Apriori (MSApriori). A modification for Matrix Apriori algorithm to accommodate this modification is proposed. Experiments on large set of data bases have been conducted to validate the proposed framework. The achieved results show that there is a remarkable improvement in the overall performance of the system in terms of run time, the number of generated rules, and number of frequent items used.
This document discusses data mining and related topics. It begins by defining data mining as the process of discovering patterns in large datasets using methods from machine learning, statistics, and database systems. The document then discusses data warehouses, how they work, and their role in data mining. It describes different data mining functionalities and tasks such as classification, prediction, and clustering. The document outlines some common data mining applications and issues related to methodology, performance, and diverse data types. Finally, it discusses some social implications of data mining involving privacy, profiling, and unauthorized use of data.
This document provides an overview of key concepts in data mining including data preprocessing, data warehouses, frequent patterns, association rule mining, classification, clustering, outlier analysis and more. It discusses different types of databases that can be mined such as relational, transactional, temporal and spatial databases. The document also covers data characterization, discrimination, interestingness measures and different types of data mining systems.
Recommendation system using bloom filter in mapreduceIJDKP
Many clients like to use the Web to discover product details in the form of online reviews. The reviews are
provided by other clients and specialists. Recommender systems provide an important response to the
information overload problem as it presents users more practical and personalized information facilities.
Collaborative filtering methods are vital component in recommender systems as they generate high-quality
recommendations by influencing the likings of society of similar users. The collaborative filtering method
has assumption that people having same tastes choose the same items. The conventional collaborative
filtering system has drawbacks as sparse data problem & lack of scalability. A new recommender system is
required to deal with the sparse data problem & produce high quality recommendations in large scale
mobile environment. MapReduce is a programming model which is widely used for large-scale data
analysis. The described algorithm of recommendation mechanism for mobile commerce is user based
collaborative filtering using MapReduce which reduces scalability problem in conventional CF system.
One of the essential operations for the data analysis is join operation. But MapReduce is not very
competent to execute the join operation as it always uses all records in the datasets where only small
fraction of datasets are applicable for the join operation. This problem can be reduced by applying
bloomjoin algorithm. The bloom filters are constructed and used to filter out redundant intermediate
records. The proposed algorithm using bloom filter will reduce the number of intermediate results and will
improve the join performance.
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.
Data mining involves analyzing large amounts of data to discover patterns that can be used for purposes such as increasing sales, reducing costs, or detecting fraud. It allows companies to better understand customer behavior and develop more effective marketing strategies. Common data mining techniques used by retailers include loyalty programs to track purchasing patterns and target customers with personalized coupons. Data mining software uses techniques like classification, clustering, and prediction to analyze data from different perspectives and extract useful information and patterns.
Data mining involves extracting useful information from large datasets. It begins by analyzing simple data to develop representations, then extends this to more complex datasets. Data mining has applications in retail, banking, insurance, and medicine. The main data mining operations are predictive modeling, database segmentation, link analysis, and deviation detection. The CRISP-DM process standardizes the data mining process into business understanding, data understanding, data preparation, modeling, evaluation, and deployment phases.
This document provides an overview of artificial neural networks and their application in data mining techniques. It discusses neural networks as a tool that can be used for data mining, though some practitioners are wary of them due to their opaque nature. The document also outlines the data mining process and some common data mining techniques like classification, clustering, regression, and association rule mining. It notes that neural networks, as a predictive modeling technique, can be useful for problems like classification and prediction.
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.
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This document contains 41 questions and answers related to data warehousing and data mining. Some key topics covered include: the uses of statistics in data mining, factors to consider when selecting a sample in statistics, types of databases like relational and transactional databases, the steps in the data mining process, definitions of data cleaning and data mining, descriptive versus predictive data mining, and an overview of statistical analysis assumptions and probabilistic graphical models.
1. The document discusses various advanced data analytics techniques including data mining, online analytical processing (OLAP), pivot tables, power pivot, power view in Excel, and different types of data mining techniques like classification, clustering, regression, association rules, outlier detection, sequential patterns, and prediction.
2. It provides details on each technique including definitions, applications, and examples.
3. The key data analytics techniques covered are data mining, OLAP, pivot tables, power pivot and power view in Excel, and various classification methods for advanced data analysis.
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.
This document provides an overview of knowledge discovery and data mining in databases. It discusses how knowledge discovery in databases is the process of finding useful knowledge from large datasets, with data mining being the core step that extracts patterns from data. The document outlines the common steps in the knowledge discovery process, including data preparation, data mining algorithm selection and employment, pattern evaluation, and incorporating discovered knowledge. It also describes different data mining techniques such as prediction, classification, and clustering and their goals of extracting meaningful information from data.
Data mining involves discovering patterns and trends in large data sets. It uses techniques from statistics, mathematics, and computer science to find hidden patterns and relationships in the data. Data mining has applications in marketing, finance, manufacturing, and healthcare to gain insights from data. The data mining process involves defining the problem, preparing data, exploring and analyzing the data, building models, validating models, and deploying the best models. Issues in data mining include handling different data types, incorporating background knowledge, and protecting privacy and security. Active areas of research will continue advancing data mining techniques.
Advancing Knowledge Discovery and Data MiningRyota Eisaki
Abstract:
Knowledge discovery and data mining have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machine learning, databases, statistics, knowledge acquisition, data visualization, and high performance computing. Knowledge discovery and data mining can be extremely beneficial for the field of Artificial Intelligence in many areas, such as industry, commerce, government, education and so on. The relation between Knowledge and Data Mining, and Knowledge Discovery in Database (KDD) process are presented in the paper. Data mining theory, Data mining tasks, Data Mining technology and Data Mining challenges are also proposed. This is an belief abstract for an invited talk at the workshop.
This document provides an overview of 6 modules in an Exploratory Data Analysis for Business course offered by SJB Institute of Technology. The modules cover topics like introduction to data mining, statistical learning and model selection, linear regression, regression shrinkage methods, principal component analysis, support vector machines, and their applications in R. SJB Institute of Technology is an autonomous institute located in Bengaluru, Karnataka, India that is approved by AICTE and affiliated to Visvesvaraya Technological University.
presentation on recent data mining Techniques ,and future directions of research from the recent research papers made in Pre-master ,in Cairo University under supervision of Dr. Rabie
This document provides an introduction to data mining concepts including definitions, tasks, challenges, and techniques. It discusses data mining definitions, the data mining process including data preprocessing steps like cleaning, integration, transformation and reduction. It also covers common data mining tasks like classification, clustering, association rule mining and the Apriori algorithm. Overall, the document serves as a high-level overview of key data mining concepts and methods.
The document provides a literature review on data mining. It discusses data mining concepts such as classification and prediction. Data mining has roots in machine learning, statistics, and artificial intelligence. It involves extracting patterns from large datasets. The document outlines several uses and functions of data mining, including classification, clustering, and anomaly detection. It also gives examples of data mining applications in fields like medicine, banking, insurance, and electronic commerce.
DATA MINING IN EDUCATION : A REVIEW ON THE KNOWLEDGE DISCOVERY PERSPECTIVEIJDKP
Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where
data mining is the core of this process. Data mining can be used to mine understandable meaningful patterns from large databases and these patterns may then be converted into knowledge.Data mining is the process of extracting the information and patterns derived by the KDD process which helps in crucial decision-making.Data mining works with data warehouse and the whole process is divded into action plan to be performed on data: Selection, transformation, mining and results interpretation. In this paper, we have reviewed Knowledge Discovery perspective in Data Mining and consolidated different areas of data
mining, its techniques and methods in it.
Data Analytics Course Curriculum_ What to Expect and How to Prepare in 2023.pdfNeha Singh
In 2023, aspiring data analysts can expect comprehensive data analytics course curriculums covering essential topics like statistical analysis, data visualization, machine learning, and big data processing. To prepare for the course, brushing up on basic mathematics, programming, and data handling skills would be beneficial.
This document provides an overview of big data analytics and data visualization. It discusses key concepts like data wrangling, exploring patterns, drawing conclusions, and communicating findings. Common techniques are also summarized, including classification, clustering, association rules, and predictive analytics. Specific algorithms like decision trees, k-means clustering, and hierarchical clustering are explained. The CRISP-DM process model and applications of analytics in areas like customer understanding and process optimization are also covered at a high level. Visualization is presented as an important part of the overall analytics process.
2. Chapter Objectives
The concepts associated with data mining.
The main features of data mining
operations, including predictive modeling,
database segmentation, link analysis, and
deviation detection.
The techniques associated with the data
mining operations.
2
3. Chapter Objectives
The process of data mining.
Important characteristics of data mining
tools.
The relationship between data mining and
data warehousing.
How Oracle supports data mining.
3
4. Data Mining
The process of extracting valid, previously
unknown, comprehensible, and actionable
information from large databases and using it
to make crucial business decisions,
(Simoudis,1996).
Involves the analysis of data and the use of
software techniques for finding hidden and
unexpected patterns and relationships in sets of
data.
4
5. Data Mining
Reveals information that is hidden and
unexpected, as little value in finding patterns
and relationships that are already intuitive.
Patterns and relationships are identified by
examining the underlying rules and features in
the data.
5
6. Data Mining
Tends to work from the data up and most
accurate results normally require large
volumes of data to deliver reliable conclusions.
Starts by developing an optimal representation
of structure of sample data, during which time
knowledge is acquired and extended to larger
sets of data.
6
7. Data Mining
Data mining can provide huge paybacks for
companies who have made a significant
investment in data warehousing.
Relatively new technology, however already
used in a number of industries.
7
8. Examples of Applications of Data Mining
Retail / Marketing
– Identifying buying patterns of customers
– Finding associations among customer
demographic characteristics
– Predicting response to mailing campaigns
– Market basket analysis
8
9. Examples of Applications of Data Mining
Banking
– Detecting patterns of fraudulent credit card
use
– Identifying loyal customers
– Predicting customers likely to change their
credit card affiliation
– Determining credit card spending by
customer groups
9
10. Examples of Applications of Data Mining
Insurance
– Claims analysis
– Predicting which customers will buy new
policies
Medicine
– Characterizing patient behavior to predict
surgery visits
– Identifying successful medical therapies for
different illnesses 10
11. Data Mining Operations
Four main operations include:
– Predictive modeling
– Database segmentation
– Link analysis
– Deviation detection
There are recognized associations between the
applications and the corresponding operations.
– e.g. Direct marketing strategies use database
segmentation. 11
12. Data Mining Techniques
Techniques are specific implementations of the
data mining operations.
Each operation has its own strengths and
weaknesses.
12
13. Data Mining Techniques
Data mining tools sometimes offer a choice of
operations to implement a technique.
Criteria for selection of tool includes
– Suitability for certain input data types
– Transparency of the mining output
– Tolerance of missing variable values
– Level of accuracy possible
– Ability to handle large volumes of data
13
15. Predictive Modeling
Similar to the human learning experience
– uses observations to form a model of the
important characteristics of some
phenomenon.
Uses generalizations of ‘real world’ and ability
to fit new data into a general framework.
Can analyze a database to determine essential
characteristics (model) about the data set. 15
16. Predictive Modeling
Model is developed using a supervised learning
approach, which has two phases: training and
testing.
– Training builds a model using a large
sample of historical data called a training
set.
– Testing involves trying out the model on
new, previously unseen data to determine its
accuracy and physical performance
characteristics.
16
17. Predictive Modeling
Applications of predictive modeling include
customer retention management, credit
approval, cross selling, and direct marketing.
There are two techniques associated with
predictive modeling: classification and value
prediction, which are distinguished by the
nature of the variable being predicted.
17
18. Predictive Modeling - Classification
Used to establish a specific predetermined class
for each record in a database from a finite set
of possible, class values.
Two specializations of classification: tree
induction and neural induction.
18
21. Predictive Modeling - Value Prediction
Used to estimate a continuous numeric value
that is associated with a database record.
Uses the traditional statistical techniques of
linear regression and nonlinear regression.
Relatively easy-to-use and understand.
21
22. Predictive Modeling - Value Prediction
Linear regression attempts to fit a straight line
through a plot of the data, such that the line is
the best representation of the average of all
observations at that point in the plot.
Problem is that the technique only works well
with linear data and is sensitive to the presence
of outliers (that is, data values, which do not
conform to the expected norm).
22
23. Predictive Modeling - Value Prediction
Although nonlinear regression avoids the main
problems of linear regression, it is still not
flexible enough to handle all possible shapes of
the data plot.
Statistical measurements are fine for building
linear models that describe predictable data
points, however, most data is not linear in
nature.
23
24. Predictive Modeling - Value Prediction
Data mining requires statistical methods that
can accommodate non-linearity, outliers, and
non-numeric data.
Applications of value prediction include credit
card fraud detection or target mailing list
identification.
24
25. Database Segmentation
Aim is to partition a database into an unknown
number of segments, or clusters, of similar
records.
Uses unsupervised learning to discover
homogeneous sub-populations in a database to
improve the accuracy of the profiles.
25
26. Database Segmentation
Less precise than other operations thus less
sensitive to redundant and irrelevant features.
Sensitivity can be reduced by ignoring a subset
of the attributes that describe each instance or
by assigning a weighting factor to each
variable.
Applications of database segmentation include
customer profiling, direct marketing, and cross
selling. 26
28. Database Segmentation
Associated with demographic or neural
clustering techniques, which are distinguished
by
– Allowable data inputs
– Methods used to calculate the distance
between records
– Presentation of the resulting segments for
analysis
28
29. Link Analysis
Aims to establish links (associations) between
records, or sets of records, in a database.
There are three specializations
– Associations discovery
– Sequential pattern discovery
– Similar time sequence discovery
Applications include product affinity analysis,
direct marketing, and stock price movement. 29
30. Link Analysis - Associations Discovery
Finds items that imply the presence of other
items in the same event.
Affinities between items are represented by
association rules.
– e.g. ‘When a customer rents property for
more than 2 years and is more than 25 years
old, in 40% of cases, the customer will buy a
property. This association happens in 35%
of all customers who rent properties’.
30
31. Link Analysis - Sequential Pattern Discovery
Finds patterns between events such that the
presence of one set of items is followed by
another set of items in a database of events
over a period of time.
– e.g. Used to understand long term customer
buying behavior.
31
32. Link Analysis - Similar Time Sequence
Discovery
Finds links between two sets of data that are
time-dependent, and is based on the degree of
similarity between the patterns that both time
series demonstrate.
– e.g. Within three months of buying property,
new home owners will purchase goods such
as cookers, freezers, and washing machines.
32
33. Deviation Detection
Relatively new operation in terms of
commercially available data mining tools.
Often a source of true discovery because it
identifies outliers, which express deviation
from some previously known expectation and
norm.
33
34. Deviation Detection
Can be performed using statistics and
visualization techniques or as a by-product of
data mining.
Applications include fraud detection in the use
of credit cards and insurance claims, quality
control, and defects tracing.
34
36. The Data Mining Process
Recognizing that a systematic approach is
essential to successful data mining, many
vendor and consulting organizations have
specified a process model designed to guide the
user through a sequence of steps that will lead
to good results.
Developed a specification called the Cross
Industry Standard Process for Data Mining
(CRISP-DM).
36
37. The Data Mining Process
CRISP-DM specifies a data mining process
model that is not compliant with a particular
industry or tool.
CRISP-DM has evolved from the knowledge
discovery processes used widely in industry
and in direct response to user requirements.
37
38. The Data Mining Process
The major aims of CRISP-DM are to make
large data mining projects run more efficiently,
be cheaper, more reliable, and more
manageable.
CRISP-DM is a hierarchical process model. At
the top level, the process is divided into six
different generic phases, ranging from business
understanding to deployment of project
results.
38
39. The Data Mining Process
The next level elaborates each of these phases
as comprising of several generic tasks. At this
level, the description is generic enough to cover
all the DM scenarios.
The third level specialises these tasks for
specific situations. For instance, the generic
task might be cleaning data, and specialised
task could be cleaning of numeric values or
categorical values.
39
40. The Data Mining Process
The fourth level is the process instance; that is
a record of actions, decisions and result of an
actual execution of DM project.
The model also discusses relationships between
different DM tasks. It gives idealised sequence
of actions during a DM project.
40
42. Data Mining Tools
There are a growing number of commercial
data mining tools on the marketplace.
Important characteristics of data mining tools
include:
– Data preparation facilities
– Selection of data mining operations
– Product scalability and performance
– Facilities for understanding results
42
43. Data Mining Tools
Data preparation facilities
– Data preparation is the most time-
consuming aspect of data mining.
– Functions supported include: data
preparation, data cleansing, data describing,
data transforming and data sampling.
43
44. Data Mining Tools
Selection of data mining operations
– Important to understand the characteristics
of the operations (algorithms) to ensure that
they meet the user’s requirements.
– In particular, important to establish how the
algorithms treat the data types of the
response and predictor variables, how fast
they train, and how fast they work on new
data.
44
45. Data Mining Tools
Product scalability and performance
– Capable of dealing with increasing amounts
of data, possibly with sophisticated
validation controls.
– Maintaining satisfactory performance may
require investigations into whether a tool is
capable of supporting parallel processing
using technologies such as SMP or MPP.
45
46. Data Mining Tools
Facilities for understanding results
– By providing measures such as those
describing accuracy and significance in
useful formats such as confusion matrices,
by allowing the user to perform sensitivity
analysis on the result, and by presenting the
result in alternative ways using for example
visualization techniques.
46
47. Data Mining and Data Warehousing
Major challenge to exploit data mining is
identifying suitable data to mine.
Data mining requires single, separate, clean,
integrated, and self-consistent source of data.
47
48. Data Mining and Data Warehousing
A data warehouse is well equipped for
providing data for mining.
Data quality and consistency is a pre-requisite
for mining to ensure the accuracy of the
predictive models. Data warehouses are
populated with clean, consistent data.
48
49. Data Mining and Data Warehousing
It is advantageous to mine data from multiple
sources to discover as many interrelationships
as possible. Data warehouses contain data from
a number of sources.
Selecting the relevant subsets of records and
fields for data mining requires the query
capabilities of the data warehouse.
49
50. Data Mining and Data Warehousing
The results of a data mining study are useful if
there is some way to further investigate the
uncovered patterns. Data warehouses provide
the capability to go back to the data source.
50