Optimization Improved K-Means on Centroid Initialization process using Particle Swarm Optimization for Tsunami Prone Area Groupings

Tsunami is a high wave caused by tectonic earthquakes, volcanic eruption or landslides in the ocean.  Indonesia is one of the countries that has thousands of islands. Lots of towns is a city on the banks or waterfront city . Indonesia becomes Tsunami prone areas. Tsunami can affect damage in various sectors, namely land degradation and infrastructure, environmental damage, fatalities, even the psychological impact on the victims themselves. Therefore, it takes a clustering of tsunami-prone areas. The result of clustering can give information to the public to remain alert to the danger of the tsunami. Also, clustering of the tsunami can be used by a government to prepare policies in overcoming the danger of the tsunami. Improved K-Means is an approach that proposed in this study to clustering the tsunami prone areas. In selecting the initial centroid must be done properly to produce a high accuracy. We proposed a method to determine the initial centroid appropriately, so that can increase the accuracy. The proposed method is Particle Swam Optimization (PSO). This study also uses comparison methods, such as K-Means, K-Means Improved, and K-Means Improved PSO. This study uses silhouette coefficient to test the accuracy of the system. The result showed that the proposed method has higher accuracy than the comparison method. Silhouette coefficient generated at 0.99924223 with smaller computing time

[1]  Stefan Dech,et al.  Tsunami risk assessment in Indonesia , 2011 .

[2]  Surjeet Kumar Yadav,et al.  Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification , 2012, ArXiv.

[3]  Agus Harjoko,et al.  Penerapan Algoritma Invasive Weed Optimnization untuk Penentuan Titik Pusat Klaster pada K-Means , 2015 .

[4]  Hae-Sang Park,et al.  A simple and fast algorithm for K-medoids clustering , 2009, Expert Syst. Appl..

[5]  S. Mahapatra,et al.  Cooperative swarm based clustering algorithm based on PSO and k-means to find optimal cluster centroids , 2012, 2012 NATIONAL CONFERENCE ON COMPUTING AND COMMUNICATION SYSTEMS.

[6]  Ali Ridho Barakbah,et al.  Hierarchical K-means: an algorithm for centroids initialization for K-means , 2007 .

[7]  Dwi Jokowinarno,et al.  Mitigasi Bencana Tsunami Di Wilayah Pesisir Lampung , 2011 .

[8]  Wayan Firdaus Mahmudy.  Improved Particle Swarm Optimization untuk Menyelesaikan Permasalahan Part Type Selection dan Machine Loading pada Flexible Manufacturing System (FMS) , 2015 .

[9]  Wei Zheng,et al.  Research on selection of initial center points based on improved K-means algorithm , 2012, Proceedings of 2012 2nd International Conference on Computer Science and Network Technology.

[10]  Aa Zezen Zaenal Abidin.  Implementasi Algoritma C 4.5 Untuk Menentukan Tingkat Bahaya Tsunami , 2011 .

[11]  Andries Petrus Engelbrecht,et al.  Data clustering using particle swarm optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[12]  Liu Yi-jun,et al.  Telecom customer segmentation with K-means clustering , 2012, 2012 7th International Conference on Computer Science & Education (ICCSE).

[13]  Sawitri Subiyanto,et al.  Penentuan Resiko Dan Kerentanan Tsunami Di Kebumen Dengan Citra Alos , 2014 .

[14]  Joshua Zhexue Huang,et al.  Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values , 1998, Data Mining and Knowledge Discovery.

[15]  Fang Yuan,et al.  A new algorithm to get the initial centroids , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[16]  P. Jaganathan,et al.  An improved K-means algorithm combined with Particle Swarm Optimization approach for efficient web document clustering , 2013, 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE).

[17]  Oki Oktariadi,et al.  Penentuan Peringkat Bahaya Tsunami dengan Metode Analytical Hierarchy Process (Studi kasus: Wilayah Pesisir Kabupaten Sukabumi) , 2009 .

[18]  Ying Huang,et al.  Improved K-Means Algorithm and Application in Customer Segmentation , 2010, 2010 Asia-Pacific Conference on Wearable Computing Systems.

[19]  A. Ahmadyfard,et al.  Combining PSO and k-means to enhance data clustering , 2008, 2008 International Symposium on Telecommunications.

[20]  Wayan Firdaus Mahmudy,et al.  Hybridizing PSO with SA for Optimizing SVR Applied to Software Effort Estimation , 2016 .

[21]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..