Efficient Clustering Algorithms for Concept-drift Categorical Data

Data Mining has become one of the most active and exciting research areas in the applications of business management, marketing analysis, engineering design, science exploration, database, visualization, arti¿cial intelligence, machine learning, pattern recognition and statistics. This book introduces new concepts to find concept-drift for categorical data using Rough set theory in an efficient manner and also deals with uncertainty information. A method for data labeling using similarity and dissimilarity measures to detect concept-drift and reducing time complexity.