MINING MASSIVE DATASETS (Professional Elective – V) B.Tech. IV Year I Sem. JNTUH R-18

 Unit I: Data Mining and MapReduce

• Explain the "Statistical Limits on Data Mining" and discuss its implications for extracting meaningful insights from massive datasets.

• Analyze the benefits and challenges of utilizing the MapReduce paradigm for handling large-scale data processing.

• Implement a MapReduce algorithm for a specific data mining task (e.g., word count, frequency analysis).

• Compare and contrast different distributed file systems used in conjunction with MapReduce.

Unit II: Similarity Search and Streaming Data

• Explain the applications of near-neighbor search in data mining and discuss different shingling techniques for document comparison.

• Implement a similarity-preserving summary algorithm for a set of data points.

• Analyze the challenges of mining data streams and discuss different sampling techniques for efficiently capturing information from continuous data flows.

• Implement a filtering algorithm for identifying specific patterns or anomalies in a data stream.

Unit III: Link Analysis and Frequent Itemsets

• Explain the PageRank algorithm and its role in web search rankings.

• Discuss efficient computation methods for PageRank and analyze the impact of link spam on its accuracy.

• Compare and contrast different limited-pass algorithms for mining frequent itemsets in large datasets.

• Implement a clustering algorithm for grouping similar data points in non-Euclidean spaces.

Unit IV: Web Advertising and Recommendation Systems

• Analyze the key issues and challenges involved in online advertising systems.

• Explain the matching problem and adwords problem in online advertising and discuss practical algorithms for addressing them.

• Design and implement a simple recommendation system based on collaborative filtering techniques.

• Discuss the role of dimensionality reduction in recommendation systems and its impact on performance.

Unit V: Social Network Graph Mining

• Analyze the characteristics of social networks as graphs and discuss the challenges of mining information from them.

• Implement a clustering algorithm for identifying communities or groups within a social network graph.

• Explain the Simrank algorithm and its applications in social network analysis.

• Discuss the process of counting triangles in a social network graph and its significance for identifying closed communities.