BigFCM: Fast, Precise and Scalable FCM on Hadoop

TL;DR

BigFCM introduces a scalable, fast, and precise fuzzy clustering algorithm designed for Hadoop, significantly reducing execution time on large datasets while maintaining clustering quality.

Contribution

It presents BigFCM, a novel Hadoop-based fuzzy clustering method that leverages map-reduce and caching to enhance scalability and speed for big data applications.

Findings

Achieves several orders of magnitude reduction in execution time.

Maintains clustering quality on multi-gigabyte datasets.

Demonstrates scalability and efficiency in big data environments.

Abstract

Clustering plays an important role in mining big data both as a modeling technique and a preprocessing step in many data mining process implementations. Fuzzy clustering provides more flexibility than non-fuzzy methods by allowing each data record to belong to more than one cluster to some degree. However, a serious challenge in fuzzy clustering is the lack of scalability. Massive datasets in emerging fields such as geosciences, biology and networking do require parallel and distributed computations with high performance to solve real-world problems. Although some clustering methods are already improved to execute on big data platforms, but their execution time is highly increased for large datasets. In this paper, a scalable Fuzzy C-Means (FCM) clustering named BigFCM is proposed and designed for the Hadoop distributed data platform. Based on the map-reduce programming model, it exploits several mechanisms including an efficient caching design to achieve several orders of magnitude reduction in execution time. Extensive evaluation over multi-gigabyte datasets shows that BigFCM is scalable while it preserves the quality of clustering.