Survey of Data Mining and Applications (Review from 1996 to Now)

Abstract

The science of extracting useful information from large data sets or databases is named as data mining. Though data mining concepts have an extensive history, the term “Data Mining“, is introduced relatively new, in mid 90’s. Data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. All of these are concerned with certain aspects of data analysis, so they have much in common but each also has its own distinct problems and types of solution. The fundamental motivation behind data mining is autonomously extracting useful information or knowledge from large data stores or sets. The goal of building computer systems that can adapt to special situations and learn from their experience has attracted researchers from many fields, including computer science, engineering, mathematics, physics, neuroscience and cognitive science. As opposed to most of statistics, data mining typically deals with data that have already been collected for some purpose other than the data mining analysis. Majority of the applications presented in this book chapter uses data formerly collected for any other purposes. Out of data mining research, has come a wide variety of learning techniques that have the potential to renovate many scientific and industrial fields. This book chapter surveys the development of Data Mining through review and classification of journal articles between years 1996-now. The basis for choosing this period is that, the comparatively new concept of data mining become widely accepted and used during that period. The literature survey is based on keyword search through online journal databases on Science Direct, EBSCO, IEEE, Taylor Francis, Thomson Gale, and Scopus. A total of 1218 articles are reviewed and 174 of them found to be including data mining methodologies as primary method used. Some of the articles include more than one data mining methodologies used in conjunction with each other. The concept of data mining can be divided into two broad areas as predictive methods and descriptive methods. Predictive methods include Classification, Regression, and Time Series Analysis. Predictive methods aim to project future status before they occur. Section 2 includes definition of algorithms and the applications using these algorithms. Discussion of trends throughout the last decade is also presented in this section. Section 3 introduces Descriptive methods in four major parts; Clustering, Summarization, Association Rules and Sequence Discovery. The objective of descriptive methods is describing phenomena, evaluating characteristics of the dataset or summarizing a series of data. The application areas of each algorithm are documented in this part with discussion of the trend in descriptive methods. Section 4 describes data warehouses and lists their applications involving data mining techniques. Section 5 gives a summarization of the study and discusses future trends in data mining and contains a brief conclusion.