The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 28-452
Full Name: Lawrence Mazlack
Position: Professor
Age: ON
Sex: Male
Address: Applied Computational Intelligence Laboratory, University of Cincinnati, USA
Country: UNITED STATES
Tel: 513-556-1883
Tel prefix: 1
Fax:
E-mail address: mazlack@uc.edu
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Title of the Paper: Identifying Appropriate Methodologies and Strategies for Vertical Mining with Incomplete Data
Authors as they appear in the Paper: Faris Alqadah, Zhen Hu, Lawrence J. Mazlack
Email addresses of all the authors: mazlack@uc.edu
Number of paper pages: 10
Abstract: Many data mining methods are dependent on recognizing frequent patterns. Frequent patterns lead to the discovery of association rules, strong rules, sequential episodes, and multi-dimensional patterns. All can play a critical role in helping corporate and scientific institutions to understand and analyze their data. Patterns should be discovered in time and space efficient manner. Discovered patterns have authentic value when they accurately describe data trends; and, do not exclusively reflect noise or chance encounters. Vertical data mining algorithms key advantage is that they can outperform their horizontal counterparts in terms of both time and space efficiency. Little work has addressed how incomplete data influences vertical data mining. Consequently, the quality and utility of vertical mining algorithms results remains ambiguous as real data sets often contain incomplete data. This paper considers how to establish methodologies that deal with incomplete dat!
a in vertical mining; additionally, it seeks to develop strategies for determining the maximal utilization that can be mined from a dataset based on how much and what data is missing.
Keywords: Incomplete Data, Vertical, Data Mining, Ignorability, Efficiency, Privacy Preserving, Data Sensitivity, Maximal Utilization, Methodologies, Strategies
EXTENSION of the file: .pdf
Special (Invited) Session: Vertical Mining with Incomplete Data
Organizer of the Session: 593-679
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IP ADDRESS: 169.229.199.229
