Linear correlation discovery in databases: a data mining approach

Linear correlation discovery in databases: a data mining approach

Very little research in knowledge discovery has studied how to incorporate statistical methods to automate linear correlation discovery (LCD). We present an automatic LCD methodology that adopts statistical measurement functions to discover correlations from databases attributes. Our methodology automatically pairs attribute groups having potential linear correlations, measures the linear corre...

Expert Discovery: A web mining approach

Expert discovery is a quest in search of finding an answer to a question: “Who is the best expert of a specific subject in a particular domain within peculiar array of parameters?” Expert with domain knowledge in any field is crucial for consulting in industry, academia and scientific community. Aim of this study is to address the issues for expert-finding task in real-world community. Collabor...

Knowledge Discovery and Data Mining in Databases

Knowledge Discovery in Databases (KDD) is the process of automatic discovery of previously unknown patterns, rules, and other regular contents implicitly present in large volumes of data. Data Mining (DM) denotes discovery of patterns in a data set previously prepared in a specific way. DM is often used as a synonym for KDD. However, strictly speaking DM is just a central phase of the entire pr...

Knowledge Discovery in Databases and Data Mining

Knowledge Discovery in Textual Databases: A Concept-Association Mining Approach

Concepts are often related to short sequences of words that occur frequently together across the text collections. Such concepts convey much of the meaning in any language. Association rule mining is a powerful technique for extracting relations among concepts. From a text mining perspective, association rules have mainly been used in a traditional support-confidence framework. This approach su...