Fuzzy Based Clustering Algorithms to Handle Big Data with Implementation on Apache Spark
Neha Bharill, Aruna Tiwari, Aayushi Malviya · 2016
With the advancement in technology, a huge amount of data containing useful information, called Big Data, is generated on a daily basis. For processing such tremendous volume of data, there is a need of Big Data frameworks such as Hadoop MapReduce, Apache Spark etc. Among these, Apache Spark performs up to 100 times faster than conventional frameworks like Hadoop Mapreduce. For the effective analysis and interpretation of this data, scalable Machine Learning methods are required to overcome the space and time bottlenecks. Partitional clustering algorithms are widely adopted by researchers for clustering large datasets due to their low computational requirements. Thus, we focus on the design of partitional clustering algorithm and its implementation on Apache Spark. In this paper, we propose a partitional based clustering algorithm called Scalable Random Sampling with Iterative Optimization Fuzzy c-Means algorithm (SRSIO-FCM) which is implemented on Apache Spark to handle the challenges associated with Big Data Clustering. Experimentation is performed on several big datasets to show the effectiveness of SRSIO-FCM in comparison with a proposed scalable version of the Literal Fuzzy c-Means (LFCM) called SLFCM implemented on Apache Spark. The comparative results are reported in terms of value of F-measure, ARI, Objective function, Run-time and Scalability. The reported results show the great potential of SRSIO-FCM for Big Data clustering.