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Developing an Efficient Spectral Clustering Algorithm on Large Scale Graphs in Spark

Research Authors
Ahmed I. Taloba
Marwan R. Riad
Taysir Hassan A. Soliman
Research Department
Research Journal
2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS)
Research Rank
4
Research Publisher
IEEE
Research Vol
NULL
Research Website
cairo , egypt
Research Year
2017
Research_Pages
292-298
Research Abstract

Recently, most of the data can be represented by graph structures, such as social media, Protein-Protein Interaction, transportation system, systems biology,..., etc. Many researches have been achieved to cluster very large graphs but more efficient algorithms are required since such a process takes a long time and requires more memory. In this paper, we propose an Efficient Spectral Clustering Algorithm on Large Scale Graphs in Spark (ESCALG), using map reduce function and shuffling phases in Dijkstra's algorithm. In addition, ESCALG depends mainly on a sparse matrix as a data structure, which less time in execution. Then, GraphX is applied to deal with graph data processing and in GraphX used Pregel in computing shortest path. To test the performance of ESCALG, it is compared with Large-Scale Spectral Clustering on Graphs and Standard Spectral Clustering Algorithms using seven datasets, where ESCALG proved high efciency in terms of memory and time performance.