The CLC (Combined Location Classification) error model provides indices for overall data uncertainty in thematic spatio-temporal datasets. It accounts for the two major sources of error in such datasets, location error and classification error. The model assumes independence between error components, while recent studies revealed various degrees of correlation between error components in actual...

Searching for information on the web has attracted many research communities. Due to the enormous size of the web and low precision of user queries, finding the right information from the web is the difficult or even impossible task. Clustering, one of the most the fundamental tools in Granular Computing (GrC), offers an interesting approach to this problem. By grouping of similar documents, cl...

Classification of satellite images plays a vital role in remote sensing applications. Numerous algorithms have been developed and tested to classify a satellite image. The main purpose of these algorithms is to lessen the human efforts and errors in minimum time. Classification is performed on satellite images for various purposes. This paper presents a framework to classify a satellite image b...

The WWW is an on-line hypertextual collection, and a more sophisticated algorithm for Web page clustering may have to be based on combined term-similarity and hyperlink-similarity measures. It has been observed that nearly all currently employed techniques for document classification on the Web make use of textual information only. In addition, most of these techniques are incapable of discover...

In K-mean algorithm, every pixel in super space is required to calculate Euclidean distance for clustering, so it is one time-consuming hard work when there are a great many class centers. Improved K-mean clustering algorithm presented here can save clustering time by making initial division based on previous clustering results, and maintaining the relationship among stable classes during clust...

Two most popular approaches to facilitate searching for information on the web are represented by web search engine and web directories. Although the performance of search engines is improving every day, searching on the web can be a tedious and time-consuming task due to the huge size and highly dynamic nature of the web. Moreover, the user’s “intention behind the search” is not clearly expres...

This work addresses the task of identifying thematic correspondences across subcorpora focused on different topics. We introduce an unsupervised algorithmic framework based on distributional data clustering, which generalizes previous initial works on this task. The empirical results reveal interesting commonalities of different religions. We evaluate the results through measuring the overlap o...

We present an approach for augmenting DBpedia, a very large ontology lying at the heart of the Linked Open Data (LOD) cloud, with domain information. Our approach uses the thematic labels provided for DBpedia entities by Wikipedia categories, and groups them based on a kernel based k-means clustering algorithm. Experiments on gold-standard data show that our approach provides a first solution t...