LINK MINING PROCESS

ABSTRACT

Many data mining and knowledge discovery methodologies and process models have been developed, with varying degrees of success, there are three main methods used to discover patterns in data; KDD, SEMMA and CRISP-DM. They are presented in many of the publications of the area and are used in practice. To our knowledge, there is no clear methodology developed to support link mining. However, there is a well known methodology in knowledge discovery in databases, known as Cross Industry Standard Process for Data Mining (CRISPDM), developed by a consortium of several industrial companies which can be relevant to the study of link mining. In this study CRISP-DM has been adapted to the field of Link mining to detect anomalies. An important goal in link mining is the task of inferring links that are not yet known in a given network. This approach is implemented through the use of a case study of realworld data (co-citation data). This case study aims to use mutual information to interpret the semantics of anomalies identified in co-citation, dataset that can provide valuable insights in determining the nature of a given link and potentially identifying important future link relationships.

KEYWORDS

Link mining, anomalies, mutual information