P3C+: Projected Clustering Algorithm Evaluation on Synthetic and Real-World Data

Sergej Fries of RWTH Aachen University proposes modifications to the P3C projected clustering algorithm for large, high-dimensional data sets. The work includes the novel P3C+-MR algorithm and its simplified P3C+-MR-Light variant, both implemented in MapReduce. Their effectiveness and efficiency are evaluated on synthetic and real-world data sets.

Use Cases

• Evaluating projected clustering algorithm performance based on the described synthetic and real-world data sets.
• Benchmarking runtime and result quality of clustering algorithms for big data based on the P3C+-MR-Light evaluation.
• Developing MapReduce-based implementations for high-dimensional data mining tasks based on the proposed algorithm changes.

Strengths

• Algorithm evaluation includes both synthetic and real-world data sets.
• Proposes two distinct algorithm variants, P3C+-MR and P3C+-MR-Light, for comparison.

Limitations

• Row count, column details, and file formats are unknown, limiting suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

RWTH Aachen University

Collection Method

Algorithm research and evaluation.