K-means clustering visualization of web-based OLAP operations for hotspot data

Abstract

In the previous work we developed the web-based OLAP (On-line Analytical Processing) integrated with the data warehouse for hotspot data in Indonesia. This work aims to develop a visualization module for hotspot clusters resulted from OLAP operations including roll up and drill down. The data warehouse consists of hotspot data represented in multidimensional model with two dimensions: time and location. In the dimension time, the ordered sequence of elements from the higher-level of hierarchy to the lowest is from year, quarter, to month. Whereas, the sequence in the dimension location is from island, province, to district. The clustering algorithm we applied was K-means in which the best clustering was obtained for the size of cluster 4 with average value of SSE (sum of square error) 0.2944 for combinations of elements in the dimension time and location. Hotspot clusters are visualized in form of maps in addition to crosstabs and graphics built in the previous work. The map module in the web-based OLAP can be used to better organize and analyze the hotspot data as one of indicators for forest fires occurrence in Indonesia.