What is Data Mining? Motivating Challenges; The origins of data mining; Data Mining Tasks. Types of Data; Data Quality.
Data Preprocessing; Measures of Similarity and Dissimilarity
Preliminaries; General approach to solving a classification problem; Decision tree induction; Rule-based classifier; Nearest-neighbor classifier.
Problem Definition; Frequent Itemset generation; Rule Generation; Compact representation of frequent itemsets; Alternative methods for generating frequent itemsets.
FP-Growth algorithm, Evaluation of association patterns; Effect of skewed support distribution; Sequential patterns.
Overview, K-means, Agglomerative hierarchical clustering, DBSCAN, Overview of Cluster Evaluation.
Multidimensional analysis and descriptive mining of complex data objects; Spatial data mining; Multimedia data mining; Text mining; Mining the WWW. Outlier analysis.
Data mining applications; Data mining system products and research prototypes; Additional themes on Data mining; Social impact of Data mining; Trends in Data mining.