MCTA – 301 (A) Data Mining and Warehousing

Introduction: Data Mining: Definitions, KDD v/s Data Mining, DBMS v/s Data Mining , DM techniques,

Mining problems, Issues and Challenges in DM, DM Application areas.

Association Rules & Clustering Techniques: Introduction, Various association algorithms like A Priori,

Partition, Pincer search etc., Generalized association rules. Clustering paradigms; Partitioning algorithms

like K-Medioid, CLARA, CLARANS; Hierarchical clustering, DBSCAN, BIRCH, CURE; categorical

clustering algorithms, STIRR, ROCK, CACTUS.

Other DM techniques & Web Mining: Application of Neural Network, AI, Fuzzy logic and Genetic

algorithm, Decision tree in DM. Web Mining, Web content mining, Web structure Mining, Web Usage

Mining.

Temporal and spatial DM: Temporal association rules, Sequence Mining, GSP, SPADE, SPIRIT, and

WUM algorithms, Episode Discovery, Event prediction, Time series analysis. Spatial Mining, Spatial

Mining tasks, Spatial clustering, Spatial Trends. Data Mining of Image and Video : A case study. Image and

Video representation techniques, feature extraction, motion analysis, content based image and video

retrieval, clustering and association paradigm, knowledge discovery.

The vicious cycle of Data mining, data mining methodology, measuring the effectiveness of data mining

data mining techniques. Market baskets analysis, memory based reasoning, automatic cluster detection, link

analysis, artificial neural networks, generic algorithms, data mining and corporate data warehouse, OLA.

Reference Books :

1. Data Mining Techniques ; Arun K.Pujari ; University Press.

2. Data Mining; Adriaans & Zantinge; Pearson education.

3. Mastering Data Mining; Berry Linoff; Wiley.

4. Data Mining; Dunham; Pearson education.

5. Text Mining Applications, Konchandy, Cengage