Data mining techniques and applications an introduction

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data mining techniques and applications an introduction

Data Mining Techniques and Applications by Hongbo Du

This concise and approachable introduction to data mining selects a mixture of data mining techniques originating from statistics, machine learning and databases, and presents them in an algorithmic approach. Aimed primarily at undergraduate readers, it presents not only the fundamental principles and concepts of the subject in an easy-to-understand way, but also hands on, practical instruction on data mining techniques, that readers can put into practice as they go along using the freely downloadable Weka toolkit. Author Hongbo Du shares his years of commercial, as well as research-based, experience in the field through extensive examples and real-world case studies, highlighting how data mining solutions provided by software tools are used in practical problem solving. Covering not only traditional areas of data mining such as association, clustering and classification, this text also explains topics such as data warehousing, online-analytic processing, and text mining.
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Datamining Techniques

Data Mining Techniques and Applications an Introduction

As a result, there is a need to store and manipulate important data which can be used later for decision making and improving the activities of the business. Data Mining is the process of extracting useful information and patterns from enormous data. Data Mining includes collection, extraction, analysis, and statistics of data. Data Mining is a logical process of finding useful information to find out useful data. Once the information and patterns are found it can be used to make decisions for developing the business.

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In this blog post, I will introduce the topic of data mining. The goal is to give a general overview of what is data mining. The reasons why data mining has become popular is that storing data electronically has become very cheap and that transferring data can now be done very quickly thanks to the fast computer networks that we have today. Having a lot of data in databases is great. Having data that we cannot understand or draw meaningful conclusions from it is useless.

Introduction Over recent years the studies in proteomic, genomics and various other biological researches has generated an increasingly large amount of biological data.
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Data mining , also called knowledge discovery in databases , in computer science , the process of discovering interesting and useful patterns and relationships in large volumes of data. The field combines tools from statistics and artificial intelligence such as neural networks and machine learning with database management to analyze large digital collections, known as data sets. Data mining is widely used in business insurance, banking, retail , science research astronomy, medicine , and government security detection of criminals and terrorists.

These are some of the books on data mining and statistics that we've found interesting or useful. Good algorithm descriptions. Covers the major areas in reasonable technical detail, with several alternative algorithms presented for classification, prediction, association rule induction and cluster analysis. Good introduction to machine learning, although I found the latter part of the book from which it gets its title a bit disappointing. The book's real strength is in placing existing machine learning methods in a good technical and philosophical perspective.

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