Course outcomes

CO1: Understand the basics of data minining and its limitations.
CO2: Gain knowledge about data mining streams.
CO3: Understand the clustering techniques for data mining.
CO4: Apply the dimensionality reduction algorithm for social network analysis.

Basics of Data Mining – computational approaches – statistical limits on data mining – MapReduce – Distributed File Systems . MapReduce . Algorithms using MapReduce . Extensions to MapReduce. Mining Data Streams: The Stream Data Model – Sampling Data in a Stream – Filtering Streams. Link analysis, Frequent itemsets, Clustering, Advertising on web, Recommendation system, Mining Social-Network Graphs, Dimensionality Reduction, Large-Scale Machine Learning.

1. Jure Leskovec , Anand Rajaraman, Jeffrey David Ullman, Mining of Massive Datasets, Cambridge University Press, 2014.
2. Tom White, Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale O’Reilly Media; 4 edition , 2015.