Clustering, in data mining, is useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric based (e.g., euclidean) simi...
This paper presents an unsupervised method for discriminating among the senses of a given target word based on the context in which it occurs. Instances of a word that occur in si...
Fuzzy-clustering methods, such as fuzzy k-means and Expectation Maximization, allow an object to be assigned to multiple clusters with different degrees of membership. However, th...
Due to the structural heterogeneity of XML, queries are often interpreted approximately. This is achieved by relaxing the query and ranking the results based on their relevance to ...
Traditional similarity or distance measurements usually become meaningless when the dimensions of the datasets increase, which has detrimental effects on clustering performance. I...