Design and Implementation of Clustering techniques based Simulator

Abstract

Data mining and Knowledge discovery has several important application areas. Data mining and knowledge discovery have been topics considered at many AI, database and statistical conferences. Knowledge discovery generally refers to the process of identifying valid, novel and understandable patterns. Knowledge discovery from large databases, often called data mining, refers to the application of the discovery process on large databases or datasets. The discovery process can be broken into several steps, including: developing an understanding of the application domain; creating a target data set; data cleaning and preprocessing; finding useful features with which to represent the data; data mining to search for patterns of interest; and interpreting and consolidating discovered patterns. Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge discovery, and current and future research directions in the field.