Academia.eduAcademia.edu

Outline

Title
Abstract
Figures
Introduction
Data Mining
Data Mining and Data Warehousing
Data Mining and Olap
Data Acquisition
Data Mining in Perspective
Actors in Data Mining
Data Mining Uses
Data Mining and Machine Learning
Data Mining and Statistics
Data Analysis Tasks and Techniques
Data Mining and the Web
Methodological Considerations
Sas -The Semma Analysis Cycle
Determine Business Objectives
Determine Data Mining Goals
Data Understanding
Data Preparation
Verify Data
Collect Initial Data
Describe Data
Explore Data
Verify Data Quality
Select Data
Clean Data
Construct Data
Integrate Data
Format Data
Generate Test Design
Evaluate Results
Review Process
Review Project
Conclusions
References
All Topics
Mathematics
Statistics

DATA MINING: A CONCEPTUAL OVERVIEW

Profile image of Sohaib AlviSohaib Alvi

Abstract

This tutorial provides an overview of the data mining process. The tutorial also provides a basic understanding of how to plan, evaluate and successfully refine a data mining project, particularly in terms of model building and model evaluation. Methodological considerations are discussed and illustrated. After explaining the nature of data mining and its importance in business, the tutorial describes the underlying machine learning and statistical techniques involved. It describes the CRISP-DM standard now being used in industry as the standard for a technology-neutral data mining process model. The paper concludes with a major illustration of the data mining process methodology and the unsolved problems that offer opportunities for research. The approach is both practical and conceptually sound in order to be useful to both academics and practitioners.

Figures (12)
Typically the data warehouse architecture has three components:
Typically the data warehouse architecture has three components:
Figure 3. A High Level Web Usage Mining Process (Adapted from Srivastava, et al 2000).
Figure 3. A High Level Web Usage Mining Process (Adapted from Srivastava, et al 2000).
as illustrated in Figure 4. The SEMMA analysis cycle guides the analyst through the process of exploring the data using visual and statistical techniques, transforming data to uncover the most significant predictive variables, modeling the variables to predict outcomes, and assessing the model by testing it with new data.
as illustrated in Figure 4. The SEMMA analysis cycle guides the analyst through the process of exploring the data using visual and statistical techniques, transforming data to uncover the most significant predictive variables, modeling the variables to predict outcomes, and assessing the model by testing it with new data.
As illustrated in Figure 5. The 5A’s process methodology is similar to that of the SEMMA lalysis cycle. >RISP-DM — THE DE FACTO STANDARD FOR INDUSTRY
As illustrated in Figure 5. The 5A’s process methodology is similar to that of the SEMMA lalysis cycle. >RISP-DM — THE DE FACTO STANDARD FOR INDUSTRY
The data preparation phase covers all activities to construct the final dataset (data that will be fed into the modeling tool(s)) from the initial raw data.
The data preparation phase covers all activities to construct the final dataset (data that will be fed into the modeling tool(s)) from the initial raw data.
The data understanding phase starts with an initial data collection. It proceeds with
The data understanding phase starts with an initial data collection. It proceeds with
As shown in Figure 8, the model set first needs to be split into three components: (1)the training set, (2) the test set, and (3) the evaluation set.
As shown in Figure 8, the model set first needs to be split into three components: (1)the training set, (2) the test set, and (3) the evaluation set.
Figure 10. Example of a “Good” Lift Chart. Note that the best model is not the one with the highest lift when it is being built. It is the model that performs the best on unseen, future data.
Figure 10. Example of a “Good” Lift Chart. Note that the best model is not the one with the highest lift when it is being built. It is the model that performs the best on unseen, future data.
A confusion matrix, sometimes called a classification matrix, is used to assess the prediction accuracy of a model. It measures whether a model is confused or not; that is, whether the model is making mistakes in its predictions. Various classification rules are used in creating a confusion matrix. The classification rules that incorporate prior probabilities, posterior probabilities and misclassification costs are based on Bayesian statistical decision theory. Bayesian theory essentially revises prior probabilities based on additional available information [Sharma 1996]. The format of a confusion matrix for a two-class case with classes yes and no is shown in Figure 11. The actual vales in a confusion matrix are often represented as percentages. Whether or not a confusion matrix is “good” depends on the costs of misclassification. At the conclusion of the model building and assessment processes, the most appropriate model will be the model that meets the business objectives.
A confusion matrix, sometimes called a classification matrix, is used to assess the prediction accuracy of a model. It measures whether a model is confused or not; that is, whether the model is making mistakes in its predictions. Various classification rules are used in creating a confusion matrix. The classification rules that incorporate prior probabilities, posterior probabilities and misclassification costs are based on Bayesian statistical decision theory. Bayesian theory essentially revises prior probabilities based on additional available information [Sharma 1996]. The format of a confusion matrix for a two-class case with classes yes and no is shown in Figure 11. The actual vales in a confusion matrix are often represented as percentages. Whether or not a confusion matrix is “good” depends on the costs of misclassification. At the conclusion of the model building and assessment processes, the most appropriate model will be the model that meets the business objectives.

Related papers

DATA MINING: A TECHNIQUE WITH WIDENING APPLICATIONS AND HAVING SOME ISSUES
IJESRT Journal

Data mining, the extraction of hidden predictive information from large databases, invented as a powerful new technology with great potential to help companies and organizations to focus on the most important information in their data warehouses. Data mining uses machine learning, statistical and visualization techniques to discovery and present knowledge in a form which is easily comprehensible to humans. Various popular data mining tools and techniques are available today for supporting large amount of applications. Data mining tools predict future trends and behaviors, allowing it’s users to make proactive, knowledge-driven decisions. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. With these large amount of features and applications there are some challenging issues also which are not exclusive and are not ordered in any way. This paper presents an overview of the data mining technique, Some of its vital applications and issues needs to be addressed.

View PDFchevron_right
DATA MINING CONCEPTS AND TECHNIQUES
Vinoth Nagarajan
View PDFchevron_right
APPLICATIONS & APPROACHES: DATA MINING
Shubham Baranwal

Data mining is a concept which looks for valuable resources as exapmles from substantial measure of information i.e from a large amount of data. The thoery or the information in this paper examines few of the data mining procedures, calculations and a portion of the associations which have adjusted information mining innovation to enhance their organizations and discovered great outcomes.

View PDFchevron_right
Editorial: Data Mining in Electronic Commerce - Support vs. Confidence
César A. Astudillo

Journal of theoretical and applied electronic commerce research, 2014

View PDFchevron_right
Main Methods of Data Mining and Their Applications
Daoud Jerab, Ahmad Jerab, hatem jerab

This study explores the principal methods of data mining and their diverse applications across industries. Its purpose is to provide a comprehensive overview of key techniques-classification, clustering, association rule learning, regression, anomaly detection, text mining, and time series analysis-highlighting their theoretical foundations and real-world use cases. Methodologically, it synthesizes and analyzes existing literature, comparing algorithmic approaches such as decision trees, Naive Bayes, SVMs, k-means, DBSCAN, Apriori, FP-growth, and ARIMA models. Key findings reveal that no single method universally outperforms others; instead, optimal results often come from hybrid approaches tailored to domain-specific challenges. The study concludes that data mining remains integral for decision-making, offering predictive power, pattern discovery, and strategic insights. Its added value lies in mapping current trends, challenges, and future prospects, particularly the integration of AI and big data technologies, with implications for business intelligence, healthcare, fraud detection, and customer relationship management.

View PDFchevron_right
A Study of the Factors Considered when Choosing an Appropriate Data Mining Algorithm
Jyotir Chatterjee
View PDFchevron_right
New Patterns And Techniques In Knowledge Discovery Through Data Mining
Mohamed Abdeldaiem

International Journal of Management and Information Technology, 2013

The main focus of this paper discussion was on mining and its set of techniques used in an automated approach to exhaustively explore and bring to the surface complex relationships in very large datasets. We discussed how a decision support process can be used to search for patterns of information in data. And also discussed different techniques for finding and describing structural patterns in data as well. Knowledge Discovery is a concept that describes the process of automatically searching large volumes of data for patterns that can be considered knowledge about the data. We discussed all automatic and semi-automatic process of discovering the patterns in data that how it leads to some advantage in businesses to make knowledge driven decisions, which help the company to succeed and compete.

View PDFchevron_right
A Survey of Conceptual Data Mining and Applications
Journal of Computer Science IJCSIS

Data mining may be a process of distinguishing and extracting hidden patterns and knowledge from databases and data warehouses. It is also referred to as knowledge Discovery in Databases (KDD) and permits knowledge discovery, data analysis, and data visualization of large databases at a high level of abstraction, while not a selected premise in mind. The operation of data mining is known by employing a technique known as modeling with it to create predictions. There are various algorithms and tools on the market for this purpose. Data mining encompasses a large variety of applications ranging from business to medication to engineering. This paper provides a survey of data mining technology, its models, and task, applications, major problems, and directions for advance analysis of data mining applications. https://sites.google.com/site/ijcsis/vol-11-no-5-may-2013

View PDFchevron_right
DATA MINING: A KNOWLEDGE DISCOVERY
Ramesh M. Mirajkar

This paper addresses the discussion of impact of data mining in today's fast growing world. Data mining means the extraction of hidden predictive information from large databases. It is an interactive information discovery process that includes data acquisition, data integration, data exploration, model building, and model validation. This paper provides an overview of data mining process, some well known techniques, software and few application areas.

View PDFchevron_right
A STUDY ON DATA MINING APPROACHES
Adam baba

Data mining technology provides a user-oriented approach to novel and hidden patterns in the data.Data mining is a process which finds useful patterns from large amount of data. This technology has been successfully applied in Science, Engineering and Technology,Medical Diagnose Systems, Marketing and Finance to assist new sightings and strengthen markets. Some of the organizations have adapted this technology to progress their businesses and found outstanding results.In modern years, advances in hardware expertise have lead to an increase in the competence to store and record personal data about consumers and individuals. This has lead to concerns that the personal data may be misused for a variety of purposes. In order to lighten these concerns, a number of techniques have been recently proposed.Data mining and knowledge discovery in databases are two new research areas that investigate the automatic extraction of previously unknown patterns from large amounts of data. Recent advances in data collection, data dissemination and related technologies have inaugurated a new era of research where existing data mining algorithms should be reconsidered from a different point of view. In this paper we discussed a broad overview of some of the data mining techniques, and their usage.

View PDFchevron_right

References (55)

  1. Berry, M. J., Linoff, G. S. (2000), "Mastering Data Mining: The Art and Science of Customer Relationship Management". Wiley Computer Publishing, New York.
  2. Chung, H. M., Gray, P. (1999), "Special Section: Data Mining". Journal of Management Information Systems, (16:1),11-17.
  3. Colin, S. (2000), "The CRISP-DM Model: The New Blueprint for Data Mining", Journal of Data Warehousing, (5:4), Fall, 13-22.
  4. Fayyad, U., Piatetsky-Shapiro, G., and Smyth, R (1996). "The KDD Process for Extracting Useful Knowledge from Volumes of Data," Communications of the ACM, (39:11), pp. 27-34. Fayyad, U., (2001), "The Digital Physics of Data Mining", Communications of the ACM, March, (44:3), 62-65.
  5. Glymour, C., Madigan D., et al (1996), "Statistical Inference and Data Mining". Communications of the ACM, (39:11), 35-41.
  6. Goebel, M., Gruenwald, L. (1999), "A Survey of Data Mining and Knowledge Discovery Software Tools", ACM SIGKDD (Special Interest Group on Knowledge Discovery and Data Mining) Explorations, June, (1:1), 20-28.
  7. Gray P., Watson, H.J. (1998a), "Professional Briefings...Present and Future Directions in
  8. Data Warehousing", Database for Advances in Information Systems, Summer, (29:3), 83-90.
  9. Gray, P., Watson, H.J. (1998b), Decision Support in the Data Warehouse, Upper Saddle River, N.J. Gray, P. (1997) " Mining for Data Warehousing Gems," Information Systems Management, Winter, 82-86.
  10. Han, J., Kamber, M. (2001), Data Mining: Concepts and Techniques, Morgan-Kaufmann Academic Press, San Francisco.
  11. Hand, D. J. (1998), "Data Mining: Statistics and More?", The American Statistician, May (52:2), 112-118.
  12. Johnson, R. & Wicheren,D.W. (1998). Applied Multivariate Statistical Analysis. Prentice Hall, New York.
  13. Kennedy, R. L., Lee, Y. Roy, B. V. Reed, C. D. & Lippman, R. P. (1997). Solving Data Mining Problems Through Pattern Recognition. New Jersey: Prentice Hall Professional Technical Reference.
  14. Kosala, R., Blockeel, H. (2000), "Web Mining Research: A Survey", ACM SIGKDD (Special Interest Group on Knowledge Discovery and Data Mining) Explorations, June, (2:1), 1-10.
  15. Langley, P., Simon, H. (1995), "Applications of Machine Learning and Rule Induction", Communications of the ACM, November, 55-65.
  16. Moeller, R. A. (2001), "Distributed Data Warehousing Using Web Technology", AMACOM, New York .
  17. Peacock, P. R. (1998a) "Data Mining in Marketing: Part 1", Marketing Management, Winter, 9-18.
  18. Peacock, P. R. (1998b) "Data Mining in Marketing: Part 2", Marketing Management, Spring, 15-25.
  19. Rajagopalan, B., Krovi, R. (2002), "Benchmarking Data Mining Algorithms", Journal of Database Management, Jan-Mar, 13, 25-36
  20. Ranjit, B., Sugumaran, V. (1999), "Application of Intelligent Agent Technology for Managerial Data Analysis and Mining", Database for Advances in Information Systems, (30:1), 77-
  21. Sharma, S., "Applied Multivariate Techniques", John Wiley & Sons, Inc. (1996).
  22. Srivastava, J., Cooley, R., Deshpande, M., Tan, P., "Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data", ACM SIGKDD (Special Interest Group on Knowledge Discovery and Data Mining) Explorations, January, (1:2)
  23. Tegarden, D.J. (1999) "Business Information Visualization" Communications of AIS (1)4
  24. Wells, M. T. (1999), "Feature Extraction Construction and Selection: A Data Mining Perspective", Journal of the American Statistical Association, (94:448), 1390. Berry, M. J., Linoff, G. S. (2000), Mastering Data Mining: The Art and Science of Customer Relationship Management. Wiley Computer Publishing, New York.
  25. Cabena, P., Hadjinian, P., Stadler, R., Verhees, J., Zanasi, A. (1998), Discovering Data Mining: From Concept to Implementation. Upper Saddle River, NJ: Prentice Hall. Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R, Editors (1996), Advances in Knowledge Discovery and Data Mining, MIT Press, Cambridge, MA Fayyad, U., Piatetsky-Shapiro, G., and Smyth, R (1996). "The KDD Process for Extracting Useful Knowledge from Volumes of Data," Communications of the ACM, (39:11), pp. 27-34.
  26. Groth, R. (1998) Data Mining: A Hands-on Approach for Business Professional, Upper Saddle River, NJ Piatetsky-Shapiro, G and Frawley W. J. (1991), Knowledge Discovery in Databases, MIT Press, Cambridge, MA A SLIGHTLY MORE TECHNICAL APPROACH TO DATA MINING Agrawal, R., Imielinski, T., Swami, A.(1993), "Database Mining: A Performance Perspective, IEEE Transactions Knowledge and Data Engineering, (5), 914-925.
  27. Chen, M.-S., Jan, J., Yu, P.S. (1996) "Data Mining: An Overview from a Database Perspective", IEEE Transactions on Knowledge and Data Engineering", (8:6), 866.883. Fayyad, U., (2001), "The Digital Physics of Data Mining", Communications of the ACM, March, (44:3), 62-65.
  28. Han, J., Kamber, M. (2001), Data Mining: Concepts and Techniques, Morgan-Kaufmann Academic Press, San Francisco.
  29. Lee, C. (2001), "The GeneMine System for Genome/Preteome Annotation and Collaborative Data Mining", IBM Systems Journal, (40:2), 592-604.
  30. Moore, A., Lee, M. S. (1998), "Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets", Journal of Artificial Intelligence Research, (8), 67.91.
  31. Witten, I. H. (2000), Data Mining : Practical Machine Learning Tools and Techniques With Java Implementations, Morgan Kaufman, San Francisco. EMPIRICAL DATA MINING STUDIES Lee, K.C. Hall, I. & Kwon, Y. (1996). "Hybrid Neural Network Models For Bankruptcy Predictions", Decision Support Systems, 18(1), 63-73.
  32. Kumar, N. K., R. & Rajagopalan, B. (1997), "Financial Decision Support With Hybrid Genetic And Neural Based Modeling Tools", European Journal of Operational Research, 103(2), 339-349.
  33. Nazem, S. & Shin, B. (1999), "Data Mining: New Arsenal For Strategic Decision Making", Journal of Database Management. 10(1), 39-42.
  34. Brachman, R.J. Khabaza, T. Kloesgen, W. Piatetsky-Shapiro, G. & Simoudis, E. (1996). "Mining Business Databases", Communications of the ACM, 39(11), 42-48.
  35. Perkowitz, M. (1999), "Towards Adaptive Web Sites: Conceptual Framework and Case Study", Computer Networks, 1245-1261.
  36. Ranjit, B., Sugumaran, V. (1999), "Application of Intelligent Agent Technology for Managerial Data Analysis and Mining", Database for Advances in Information Systems, (30:1), 77- 94. Spangler, W. E.; May, J. H., Vargas, L. G. (1999), "Choosing Data-Mining Methods For Multiple Classification: Representational And Performance Measurement Implications For Decision Support ",Journal of Management Information Systems, Summer, 37-62.
  37. Tam, K.Y. & Kiang, M.Y. (1992). "Managerial Applications Of Neural Networks: The Case Of Bank Failure Predictions", Decision Sciences, 38(7), 926-948.
  38. DATA MINING FROM A STATISTICAL PERSPECTIVE Glymour, C., Madigan D., et al (1996), "Statistical Inference and Data Mining". Communications of the ACM, (39:11), pp. 35-41.
  39. Hand, D. J. (1998), "Data Mining: Statistics and More?", The American Statistician, May (52:2), 112-118.
  40. Moore, A., Lee, M. S. (1998), "Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets", Journal of Artificial Intelligence Research, (8), 67.91.
  41. Wells, M. T. (1999), "Feature Extraction Construction and Selection: A Data Mining Perspective", Journal of the American Statistical Association, (94:448), p. 1390. Ye, Jianming (1998), "On Measuring and Correcting the Effects of Data Mining and Model Selection", Journal of the American Statistical Association, (93:441), 120-131
  42. Data Mining: A Conceptual Overview by J. Jackson DATA MINING FROM A DATABASE/DATA WAREHOUSING PERSPECTIVE Agrawal, R., Imielinski, T., Swami, A.(1993), "Database Mining: A Performance Perspective, IEEE Transactions Knowledge and Data Engineering, (5), 914-925.
  43. Chen, M.-S., Jan, J., Yu, P.S. (1996) "Data Mining: An Overview from a Database Perspective", IEEE Transactions on Knowledge and Data Engineering", (8:6), 866.883. Moeller, R. A. (2001), "Distributed Data Warehousing Using Web Technology", AMACOM, New York .
  44. Gray P., Watson, H.J. (1998a), "Professional Briefings...Present and Future Directions in
  45. Data Warehousing", Database for Advances in Information Systems, Summer, (29:3), 83-90.
  46. Gray, P., Watson, H.J. (1998b), Decision Support in the Data Warehouse, Upper Saddle River, N.J. Gray, P. (1997)"BOOKISMS: Mining for Data Warehousing Gems," Information Systems Management, Winter, 82-86.
  47. Moeller, R. A. (2001), Distributed Data Warehousing Using Web Technology, American Management Association (AMACOM) , New York.
  48. DATA MINING FROM A MACHINE LEARNING PERSPECTIVE Burges, C. J. (1998), "A Tutorial on Support Vector Machines for Pattern Recognition", Data Mining and Knowledge Discovery, (2:2), Kennedy, R. L. Lee, Y. Roy, B. V. Reed, C. D. & Lippman, R. P. (1997). Solving Data Mining Problems Through Pattern Recognition. New Jersey: Prentice Hall Professional Technical Reference.
  49. Langley, P., Simon, H. A. (1995), "Application of Machine Learning and Rule Induction", Communications of the ACM, (38:11), 55.64.
  50. Moore, A., Lee, M. S. (1998), "Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets", Journal of Artificial Intelligence Research, (8), 67.91.
  51. Witten, I. H. (2000), Data Mining : Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufman, San Francisco. DATA MINING FROM AN INTERNET/WEB-BASED PERSPECTIVE Edelstein, H., A. (2001), "Pan for Gold in the Clickstream", Informationweek.com, March 12,2001, 77-91
  52. Lee, C. (2001), "The GeneMine System for Genome/Preteome Annotation and Collaborative Data Mining", IBM Systems Journal, (40:2), 592-604.
  53. Moeller, R. A. (2001), Distributed Data Warehousing Using Web Technology, American Management Association (AMACOM) , New York.
  54. Perkowitz, M. (1999), "Towards Adaptive Web Sites: Conceptual Framework and Case Study", Computer Networks, 1245-1261.
  55. Srivastava, J., Cooley, R., Deshpande, M., Tan, P., " Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data", ACM SIGKDD (Special Interest Group on Knowledge Discovery and Data Mining) Explorations, January, (1:2).

Related papers

A Conceptual Overview of Data Mining
yitayew solomon

—Data mining an non-trivial extraction of novel, implicit, and actionable knowledge from large data sets is an evolving technology which is a direct result of the increasing use of computer databases in order to store and retrieve information effectively .It is also known as Knowledge Discovery in Databases (KDD) and enables data exploration, data analysis, and data visualization of huge databases at a high level of abstraction, without a specific hypothesis in mind. The working of data mining is understood by using a method called modeling with it to make predictions. Data mining techniques are results of long process of research and product development and include artificial neural networks, decision trees and genetic algorithms. This paper surveys the data mining technology, its definition, motivation, its process and architecture, kind of data mined, functionalities and classification of data mining, major issues, applications and directions for further research of data mining technology.

View PDFchevron_right
A Review of Data Mining Literature
Majid Zaman, Journal of Computer Science IJCSIS

With progression in technology specifically in last three decades or so, an enormous magnitude of information has been transitioned into a digital form, which resulted in formation of enormous data repositories. With accrual of information in these repositories a challenge persisted as how to extract meaningful knowledge from it. Data mining as a tool was used to tackle the situation. Data mining considered as stepping stone to procedure of knowledge discovery in databases, this is a procedure of extracting hidden information from enormous sets of databases to excavate eloquent patterns and rules. Data mining has now become an indispensable component in almost every field of human life. The present article provides an analysis of the available literature on data mining. The concept of data mining as well as its various methodologies are summarized. Some applications, tasks and issues related to it have also been illustrated.

View PDFchevron_right
Methodological and practical aspects of data mining
Hennie Daniels

Information & Management, 2000

We describe the different stages in the data mining process and discuss some pitfalls and guidelines to circumvent them. Despite the predominant attention on analysis, data selection and pre-processing are the most time-consuming activities, and have a substantial in¯uence on ultimate success. Successful data mining projects require the involvement of expertise in data mining, company data, and the subject area concerned. Despite the attractive suggestion of`fully automatic' data analysis, knowledge of the processes behind the data remains indispensable in avoiding the many pitfalls of data mining. #

View PDFchevron_right
Data mining: concepts and techniques by Jiawei Han and Micheline Kamber
Sonali Chandrashekhar Patil-Sonawane

Sigmod Record, 2002

Mining information from data: A presentday gold rush. Data Mining is a multidisciplinary field which supports knowledge workers who try to extract information in our "data rich, information poor" environment. Its name stems from the idea of mining knowledge from large amounts of data. The tools it provides assist us in the discovery of relevant information through a wide range of data analysis techniques. Any method used to extract patterns from a given data source is considered to be a data mining technique.

View PDFchevron_right
A Critical Overview of Data Mining for Business Applications
Peter Groumpos

2017

Over the past 2-3 decades there has been a huge increase in the amount of data being stored in databases as well as the number of database applications in business and the scientific domain. This explosion in the amount of electronically stored data was accelerated by the success of the relational model for storing data and the development and maturing of data retrieval and manipulation technologies. While technology for storing the data developed fast to keep up with the demand, little stress was paid to developing software for analysing the data until recently when companies realized that hidden within these masses of data was a resource that was being ignored. The huge amounts of stored data contains knowledge on a good number of aspects of their business waiting to be harnessed and used for more effective business decision support. Data mining methods seem very appropriate to extract this useful information. A good number of them are presented and briefly analyzed. Possible applications in utilizing these techniques are outlined. An overview of both the data mining techniques and potential application to solve many challenging problems of the society is been carefully analyzed and presented. Very interested future research directions are briefly presented.

View PDFchevron_right

Related topics

  • Statistics
  • Machine Learning
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved