Volume: 19 Issue-01 (January-June) 2024

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EFCM-FFO: A NOVEL HYBRID ENHANCED FUZZY C-MEANS CLUSTERING BASED FRUIT FLY OPTIMIZATION IN HADOOP MAPREDUCING MODEL

ABSTRACT

The clustering of Bigdata is a common task in data mining and machine learning. The goal is to group similar data points to identify patterns and relationships in the data. However, clustering large datasets can be computationally expensive and time-consuming. K-Means Clustering is a very powerful and frequently used algorithm for the clustering, it has got its own limitation. Hadoop is a sophisticated framework that facilitates the distributed processing of voluminous datasets across multiple clusters of computers. MapReduce is a programming model that simplifies the processing of large datasets by breaking them down into smaller chunks and processing them in parallel across the cluster. One approach to Enhanced Fuzzy C-Means clustering Bigdata using Hadoop MapReduce is to use a genetic algorithm. A Fruit Fly Optimization (FFO) algorithm finds the wellness of the populace to choose the optimal C values as far as execution time and classification error. The experiments show that the improved algorithm is generally applicable to the clustering of different shape class clusters and larger scale data and has obvious improvement in accuracy and parallel efficiency.Two datasets, namely localization and skin segmentation datasets, are used for theexperimentation and the performance is evaluated regarding two performance evaluation metrics: clusteringaccuracy and DB-index. The maximum accuracy attained by the proposed EFCM-FFO technique is 87.91%and 90% for the localization and skin segmentation datasets, respectively, thus proving its effectiveness inbig data clustering.

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