A K-partitioning algorithm for clustering large-scale spatio-textual data. (March 2017) - Choi, Dong-Wan; Chung, Chin-Wan
Abstract: The volume of spatio-textual data is drastically increasing in these days, and this makes more and more essential to process such a large-scale spatio-textual dataset. Even though numerous works have been studied for answering various kinds of spatio-textual queries, the analyzing method for spatio-textual data has rarely been considered so far. Motivated by this, this paper proposes a k -means based clustering algorithm specialized for a massive spatio-textual data. One of the strong points of the k -means algorithm lies in its efficiency and scalability, implying that it is appropriate for a large-scale data. However, it is challenging to apply the normal k -means algorithm to spatio-textual data, since each spatio-textual object has non-numeric attributes, that is, textual dimension, as well as numeric attributes, that is, spatial dimension. We address this problem by using the expected distance between a random pair of objects rather than constructing actual centroid of each cluster. Based on our experimental results, we show that the clustering quality of our algorithm is comparable to those of other k - partitioning algorithms that can process spatio-textual data, and its efficiency is superior to those competitors. Abstract : Highlights: The problem of clustering large-scale spatio-textual data is firstly studied. It has many real applications like location-based data cleaning. A modified version of the k-means clustering algorithm is developed for spatio-textual data using the expected pairwise distance. Experimentally, our algorithm is not only fast enough to tackle a massive spatio-textual dataset, but also fairly effective in terms of the quality.