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Scalable DBSCAN Implementation
Parallel DBSCAN on Spark is a distributed spatial data mining project
focused on improving the scalability and efficiency of the DBSCAN clustering algorithm
using Apache Spark. The project leverages distributed partitioning,
KD-tree based neighbor search, ghost point replication,
and parallel cluster merging techniques to process
large scale spatial datasets efficiently.
Responsibilities
Designed and implemented a distributed version of the DBSCAN clustering algorithm
on Apache Spark for scalable spatial data mining.
Developed grid based partitioning strategies and ghost point replication
mechanisms to preserve cluster continuity across partition boundaries.
Optimized neighbor search performance using KD-tree indexing,
replacing naive O(n²) range queries with efficient spatial indexing.
Implemented distributed cluster merging using Union-Find techniques
to maintain global cluster consistency across partitions.
Worked on improving execution efficiency, distributed processing,
and scalability for large spatial datasets using parallel computation.
Technologies & Domains
Apache Spark
Distributed Systems
DBSCAN
Spatial Data Mining
Parallel Processing
KD-Tree
Union-Find
Grid Partitioning
Scalable Computing
Big Data Processing
Cluster Analysis
Data Mining