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標題: 於Spark上實作一個有效率的具有基底層的珊瑚礁最佳化演算法並使用在分群問題上
A High Performance Coral Reefs Optimization with Substrate Layers for Clustering Problem on Spark
作者: 王翊仲 
Yi-Chung Wang 
關鍵字: 資料分群;超啟發式演算法;珊瑚礁最佳化演算法;data clustering;metaheuristic algorithm;coral reef optimization
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近幾年中有許多研究結果顯示,從日常所搜集到的大量資料中,可能找出有價值的資訊。人類可藉此得到更多的輔助,以做出更適當的決策。所以發展一個巨量資料分析系統,在現代成為一個熱門的研究議題。在資料探勘 (data mining) 的領域中,如何分析這些數據被認為是非常有前景的研究。資料分群 (data clustering) 為其中代表性的研究之一,它常被用在將未知的資料做分類。近期許多研究嘗試使用超啟發式演算法 (metaheuristic algorithm) 來解決資料分群問題,進而得到比傳統分群方法更好的結果。在本篇論文中,我們基於具有基底層的珊瑚礁最佳化演算法 (coral reefs optimization with substrate layers) 進行改良,進一步提出了一個高性能分群演算法,並將其實作在Apache Spark平台之上,以減少該演算法的計算時間。在本篇論文的實驗中,我們使用平方誤差總和 (sum of squared errors) 當作衡量標準,並將該演算法與k-means演算法、基因k-means演算法 (genetic k-means algorithm)、粒子群最佳化演算法 (particle swarm optimization),以及一般的珊瑚礁最佳化演算法 (coral reefs optimization) 進行比較,比較結果表明,本篇論文所提出的方法,可以有效減少演算法的計算時間,以及找到比其他分群演算法更佳的分群結果。

Since many successful results in recent years show that it is possible to find out valuable information from daily data. Human can make more appropriate decisions by these valuable information. To develop a 'good' data analysis system for data deluge has become a popular research topic. 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 than traditional clustering algorithms. In this paper, we present a high performance clustering algorithm based on coral reefs optimization with substrate layers (CRO-SL). To reduce the computation time of the proposed algorithm, we also have implemented it on Apache Spark. In experimental results, we compare the proposed algorithm with k-means algorithm, genetic k-means algorithm (GKA), particle swarm optimization (PSO), and simple CRO algorithm in terms of the sum of squared errors (SSE). The simulation results show that the proposed algorithm can reduce the computation time significantly and also can provide a better clustering result than the other clustering algorithms.
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