Author
: Dr. Zakea Il-agure
Affiliation
: Higher Colleges of Technology
Country
: United Arab Emirates
Category
: Computer Science & Information
Technology
Volume,
Issue, Month, Year : 7, 5, April, 2017
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 real world 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.
Keyword
: Link mining, anomalies, mutual
information
For
More Details : https://airccj.org/CSCP/vol7/csit76709.pdf
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