Fuzzy Based Clustering Algorithms to Handle Big Data with Implementation on Apache Spark

Bharill, Neha; Tiwari, Aruna; Malviya, Aayushi

doi:10.1109/bigdataservice.2016.34

Cited by 17 publications

(2 citation statements)

References 17 publications

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“…Speedup is obtained between Apache Spark's standalone runtime on the Apache Spark cluster with 2 workers that are relatively the same, namely 1.003, cluster speedup with 4 workers of 2.913, and speedup back in the cluster with 7 workers of 5.85. Examples of speedup calculations in clusters with 7 workers are shown in equations ( 11) and (12). is a speedup obtained by a cluster with 7 workers.…”

Section: Experimental Results On the Clustermentioning

confidence: 99%

Parallelization of Hybrid Content Based and Collaborative Filtering Method in Recommendation System with Apache Spark

Ikhsanudin

Winarko²

2019

Indonesian J. Comput. Cybern. Syst.

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Collaborative Filtering as a popular method that used for recommendation system. Improvisation is done in purpose of improving the accuracy of the recommendation. A way to do this is to combine with content based method. But the hybrid method has a lack in terms of scalability. The main aim of this research is to solve problem that faced by recommendation system with hybrid collaborative filtering and content based method by applying parallelization on the Apache Spark platform.Based on the test results, the value of hybrid collaborative filtering method and content based on Apache Spark cluster with 2 node worker is 1,003 which then increased to 2,913 on cluster having 4 node worker. The speedup got more increased to 5,85 on the cluster that containing 7 node worker.

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Section: Experimental Results On the Clustermentioning

confidence: 99%

Parallelization of Hybrid Content Based and Collaborative Filtering Method in Recommendation System with Apache Spark

Ikhsanudin

Winarko²

2019

Indonesian J. Comput. Cybern. Syst.

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“…Some other famous clustering algorithms have been implemented in Spark framework but they do not belong to core Spark libraries, e.g. CURE [7] and a scalable random sampling variation of fuzzy c-Means [8].…”

Section: Introductionmentioning

confidence: 99%

Scalable implementation of dependence clustering in Apache Spark

Ivannikova¹

2017

2017 Evolving and Adaptive Intelligent Systems (EAIS)

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Abstract-This article proposes a scalable version of the Dependence Clustering algorithm which belongs to the class of spectral clustering methods. The method is implemented in Apache Spark using GraphX API primitives. Moreover, a fast approximate diffusion procedure that enables algorithms of spectral clustering type in Spark environment is introduced. In addition, the proposed algorithm is benchmarked against Spectral clustering. Results of applying the method to real-life data allow concluding that the implementation scales well, yet demonstrating good performance for densely connected graphs.

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Spark Based ANFIS Approach for Anomaly Detection Using Big Data

Santosh

Ramesh

2018

Communications in Computer and Information Science

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Fuzzy Based Clustering Algorithms to Handle Big Data with Implementation on Apache Spark

Cited by 17 publications

References 17 publications

Parallelization of Hybrid Content Based and Collaborative Filtering Method in Recommendation System with Apache Spark

Parallelization of Hybrid Content Based and Collaborative Filtering Method in Recommendation System with Apache Spark

Scalable implementation of dependence clustering in Apache Spark

Spark Based ANFIS Approach for Anomaly Detection Using Big Data

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