A web based data mining courseware

An immense amount of data are stored in files, databases and other repositories, therefore it is increasingly important to develop powerful means for analysis of data and the extraction of knowledge that could help us to make better decisions. Data mining, one of the key steps of Knowledge Discovery in Databases (KDD) is the process of making better decisions by looking to the database record patterns and behavior of those records. It uses statistical and pattern matching techniques to find the useful patterns from large number of records. Data mining tools allow making effective and knowledge driven decisions. Nowadays many companies use data mining approaches to improve their marketing approach. The main objective of this project is to provide a web interactive courseware for learning data mining concepts and make it available to students to learn those concepts of data mining tools. This courseware will be a medium for students to understand examples and solutions of clustering techniques such as Dendrogram (Single Link, Complete Link and Average Link), K-mean and EM algorithm and data mining tools such as WEKA and RapidMiner. In this courseware, the focus is on clustering techniques and data mining tools. The courseware is designed to be integrated with CSC 177 Data Warehousing and Data Mining course website at CSU Sacramento