Download MCA Syllabus 2013 - Christ University

Syllabus 2013MCAdocuments. Map Reduce: Distributed file systems, Map and reduce tasks. Algorithms that usemap-reduce: Matrix vector multiplication, Relational algebra operations.Mining Frequent Patterns and Associations: Near-neighbor search, Collaborative filtering,Shingling. Min-hashing and locality sensitive hashing.Unit IV (13)The stream data model, examples of stream sources and queries, sampling data in a stream.Filtering streams, bloom filters, counting distinct elements in a stream. Market-Basket model,Association rules. A-priori algorithm.Classification: Introduction to text classification. NaïveBaye’s models. Spam filtering. K nearest neighbors, Decision boundaries, vector spaceclassification using centroids. Comparative results. Support vector machine classifiers. Kernelfunction. Evaluation of classification. Micro-and macro-averaging. Learning rankings.Unit V. (13)Clustering: Introduction to the problem. Partitioning methods: K-means clustering;Hierarchical clustering. Latent semantic indexing (LSI). Applications to clustering and toinformation retrieval.Web Mining: Introduction to web . Web search overview, web structure,the user, paid placement, search engine optimization/spam. Web measurement. Crawling andweb indexes. Near-duplicate detection. Link analysis. Web as a graph. PageRank. Machinelearning techniques for ranking.Text Book:[1] C. Manning, P. Raghavan, and H. Schütze, .“ Introduction to Information Retrieval“,Cambridge University Press, 2008.[2] Anand Rajaraman and Jeffery D.ullman,”Mining the Massive”,Cambrize University Press,2008.Reference Books:[1] Data,Bing Liu, .”Web Data Minig,Exploring Hyperlinks,contents and usage”,2 ndEdition, July 2011,Springer.[2] K.P Soman, Shyam diwakar,VAjay, Insight into Data Mining – Theory and Practice, 6 thprint, PHI India, 2012[2] Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, 2 nd Edition,2006, Morgan Kaufmann Publishers, San Francisco, USA.Christ University, Bangalore, India 106