 International Journal of Data Mining, Modelling and Management (IJDMMM)
 Call For papers Special Issue on: "Interesting Knowledge Mining"
Guest Editors:
Dr. Li-Shiang Tsay, North Carolina A&T State University, USA
Dr. Seunghyun Im, University of Pittsburgh at Johnstown, USA
Dr. Zbigniew W. Ras, University of North Carolina at Charlotte, USA
The ultimate goal of knowledge discovery (KD) is to extract sets of patterns leading to useful knowledge for obtaining user desirable outcomes. The key characteristic of knowledge usefulness is that these patterns are actionable. In the last decade, KD algorithms such as mining for association rules, clustering, and classification rules, have made tremendous progress and have demonstrated significant value in a variety of real-world data mining applications. These various techniques can learn rules that summarise the data but can not model specific actions to achieve the users’ goals. A large gap remains between the results a KD system provides and taking actions based on the discovered patterns. Currently, this gap is filled by manual or semi-automatic analysis which is time consuming, biased, and limits the efficiency of the knowledge discovery in databases overall process and capabilities.
This special issue will attempt to address this gap by publishing any efforts to
- diminish the gap between the discovered results and actual action plans and
- facilitate human beings in evaluating and interpreting the discovered patterns.
The papers in this special issue aim to represent the latest knowledge mining techniques, which can be used to extract actionable knowledge and demonstrated its use as a weapon to outmanoeuvre competitors.
We welcome theoretical, empirical papers, and interesting case studies that are within the scope of this issue. The issue will contain invited papers and papers submitted directly as per instruction below. If the number of accepted papers exceeds the needs of the special issue, they will appear in a regular IJDMMM issue.
 Subject Coverage
Topics of interest include, but are not limited to:
- Intelligent agent technology
- Intelligent information systems
- Knowledge representation and integration
- Knowledge discovery and data mining
- Knowledge visualisation
Notes for Prospective Authors
Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere
All papers are refereed through a peer review process. A guide for authors, sample copies and other relevant information for submitting papers are available on the Author Guidelines page
Important Dates
Submission due date of full paper: 15 November, 2009
Feedback from referees: 5 January, 2009
Submission due date of revised paper: 30 January, 2010
Notification of acceptance: 1 March, 2010
Submission of final revised paper: 25 March, 2010 Editors and Notes
Prospective authors are welcome to submit an abstract to the guest editors for preliminary feedback on the appropriateness of their planned manuscript. You may send one copy of the full manuscript in the form of an MS Word or PDF file attached to an e-mai (details in Author Guidelines) to the following:
Dr. Li-Shiang Tsay
School of Technology
North Carolina A&T State University
Greensboro, NC, 27411
USA
Email: lishiangtsay@yahoo.com
Dr. Seunghyun Im
University of Pittsburgh at Johnstown
Johnstown, PA, 15904
USA
Email: sim@pitt.edu
Please include in your submission the title of the Special Issue, the title of the Journal and the name of the Guest Editor |
