Jure Leskovec of Stanford University authored a book focusing on practical algorithms for data mining key problems in extremely large datasets from Web and Internet commerce. The third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs. It discusses the MapReduce framework, locality-sensitive hashing, stream-processing algorithms, PageRank, frequent itemset discovery, and clustering.
Use Cases
- Learning about the MapReduce framework for parallelizing algorithms automatically based on the book's discussion.
- Applying locality-sensitive hashing tricks for similarity search as explained in the text.
- Implementing stream-processing algorithms for mining high-velocity data that arrives too fast for exhaustive processing.
- Understanding the PageRank idea and related tricks for organizing the Web as covered in a chapter.
- Studying algorithms for finding frequent itemsets and clustering as key data mining problems.
Strengths
- Authored by Jure Leskovec, a leading authority from Stanford University.
- Focuses on practical algorithms that have been used to solve key problems in data mining.
- Third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.
Limitations
- Description metadata is limited; actual data quality requires manual inspection after download.
- Row count is unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
Provenance
- Source
- Stanford University
- Collection Method
- Authored book compilation.