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 KMethod,
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, OLAP
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
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