06CS755 - Data Mining |
PART – A |
UNIT 1 |
INTRODUCTION, DATA – 1: What is Data Mining? Motivating
Challenges; The origins of data mining; Data Mining Tasks. Types of Data;
Data Quality. |
UNIT 2 |
DATA – 2: Data Preprocessing; Measures of Similarity and Dissimilarity |
UNIT 3 |
CLASSIFICATION: Preliminaries; General approach to solving a
classification problem; Decision tree induction; Rule-based classifier;
Nearest-neighbor classifier. |
UNIT 4 |
ASSOCIATION ANALYSIS - 1: Problem Definition; Frequent Itemset
generation; Rule Generation; Compact representation of frequent itemsets;
Alternative methods for generating frequent itemsets. |
PART – B |
UNIT 5 |
ASSOCIATION ANALYSIS – 2 : FP-Growth algorithm, Evaluation of
association patterns; Effect of skewed support distribution; Sequential
patterns. |
UNIT 6 |
CLUSTER ANALYSIS: Overview, K-means, Agglomerative hierarchical
clustering, DBSCAN, Overview of Cluster Evaluation. |
UNIT 7 |
FURTHER TOPICS IN DATA MINING: Multidimensional analysis and
descriptive mining of complex data objects; Spatial data mining; Multimedia
data mining; Text mining; Mining the WWW. Outlier analysis. |
UNIT 8 |
APPLICATIONS: Data mining applications; Data mining system products
and research prototypes; Additional themes on Data mining; Social impact of
Data mining; Trends in Data mining. |
REFERENCE |
TEXT BOOKS: |
1. Introduction to Data Mining – Pang-Ning Tan, Michael Steinbach,
Vipin Kumar, Pearson Education, 2007.
2. Data Mining – Concepts and Techniques – Jiawei Han and
Micheline Kamber, 2nd Edition, Morgan Kaufmann, 2006.
|
Reference Books |
1. Insight into Data Mining – Theory and Practice – K.P.Soman,
Shyam Diwakar, V.Ajay, PHI, 2006. |