Document clustering has been investigated for use in a number of different areas of text mining and information retrieval. Initially, document clustering was investigated for improving the precision or recall in information retrieval systems and as an efficient way of finding the nearest neighbors of a document. More recently, clustering has been proposed… (More)
Document clustering generates clusters from the whole document collection automatically and is used in many fields, including data mining and information retrieval. Clustering text data faces a number of new challenges. Among others, the volume of text data, dimensionality, sparsity and complex semantics are the most important ones. These characteristics of… (More)
This paper sums up the applications of Statistical model such as ARIMA family time series models in Canadian lynx data time series analysis and introduces the method of data mining combined with Statistical knowledge to analysis Canadian lynx data series.
We present a new query formulation interface called FFQI (Fast Formulation Query Interface) which is based on a semantic graph model. The query formulator allows the users with limited IT skills to query and explore the data source easily and efficiently. Here the user inputs are formulated based on the graph search algorithm by using the probabilistic… (More)