When data sets are analyzed, statistical pattern recognition is often used to find the information hidden in the data. Another approach to information discovery is data mining. Dat...
Data mining introduces new problems in database security. The basic problem of using non-sensitive data to infer sensitive data is made more difficult by the "prob abilistic&...
s In data mining, we emphasize the need for learning from huge, incomplete and imperfect data sets (Fayyad et al. 1996, Frawley et al. 1991, Piatetsky-Shapiro and Frawley, 1991). T...
Data mining is a new, important and fast growing database application. Outlier (exception) detection is one kind of data mining, which can be applied in a variety of areas like mon...
Currently statistical and artificial neural network methods dominate in financial data mining. Alternative relational (symbolic) data mining methods have shown their effectiveness...
Spam, also known as Unsolicited Commercial Email (UCE), is the bane of email communication. Many data mining researchers have addressed the problem of detecting spam, generally by...
Data mining (DM) techniques are being increasingly used in many modern organizations to retrieve valuable knowledge structures from organizational databases, including data wareho...
: In this paper we propose an application of data mining methods in the prediction of the availability and performance of Internet paths. We deploy a general decision-making method...
: Rule mining is an important issue in data mining. To address it, a novel Immune Gene Expression Programming (IGEP) model was proposed. Concepts of rule, gene, immune cell, and an...
Tao Zeng, Changjie Tang, Yong Xiang, Peng Chen, Yi...
Abstract. A viable ontology engineering methodology requires supporting domain experts in gradually building and managing increasingly complex versions of ontological elements and ...