An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Validity Assessment: Finding the Optimal Partitioning of a Data Set
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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Two clustering techniques of water quality for canals in Bangkok were compared: K-means and Fuzzy c-means. The result illustrated that K-means has a better performance. As a result, K-means cluster was used to classify 24 canals of 344 records of surface water quality within Bangkok; the capital city of Thailand. The data was obtained from the Department of Drainage and Sewerage Bangkok Metropolitan Administration during 2005-2008. Water samples were collected and analyzed on 13 different parameters: temperature, pH value (pH), hydrogen sulfide (H2S), dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), substance solid (SS), total kjeldahl nitrogen (TKN), ammonia nitrogen (NH3N), nitrite nitrogen (NO2N), nitrate nitrogen (NO3N), total phosphorous (T-P) and total coliform. The data were analyzed and clustered. The results of cluster analysis divided the canals into five clusters. The information from clustering could enhance the understanding of surface water usage in the area. Additionally, it can provide the useful information for better planning and watershed management of canals in Bangkok.