An Efficient Coral Reef Optimization with Substrate Layers for Clustering Problem on Spark
Yi‐Chung Wang, Chun‐Wei Tsai · 2018
To develop a "good" data analysis system for data deluge has become a popular research topic because many successful results show that we may be able to find out valuable information from data to make an appropriate decision. How to analyze such data has been a promising research in data mining. Data clustering is a representative research topic because its solution can be used to classify the unknown data without prior knowledge. Several recent studies attempted to use metaheuristic algorithms to solve clustering problems, and most of them provide a high-quality result. In this paper, we present a high performance clustering algorithm based on the coral reef optimization with substrate layers (CRO-SL). To reduce the computation time of the proposed algorithm, we also have it implemented on Apache Spark. The simulation results show that the proposed algorithm can provide a better clustering result than the other clustering algorithms, such as k-means algorithm, genetic k-means algorithm (GKA), and simple CRO algorithm in terms of the sum of squared errors (SSE).