원문정보
초록
영어
In the realistic situation, there are many areas which contain imprecisely specified data. This imprecise data indicates the presence of vagueness, incompleteness and uncertainty which causes the problem during important decision-making task. The present paper focuses on the problem of mining important inference from supermarket basket data (in the presence of vagueness). The paper specifically studies the usefulness of vague set theory for the exploration of hesitation information and vague association rules. The hesitation information of an item plays a vital role in making selling strategies for the exhilaration of business. For this purpose, the vague set concept can be used as an important tool which can assist in the identification of hesitated item. The vague set theory with its two membership function provides more intuitive way to interact with the vague situation that causes the hesitation for any item. The effectiveness of the hesitated pattern and rule provide advanced decision-making capabilities that transform ‘almost sold’ items to ‘sold items’.
목차
1. Introduction
2. Preliminaries
2.1. Type of Uncertainty in Data Mining
2.2. Vague Set and Intuitionistic Fuzzy Sets
2.3. Vague Set Memberships
2.4. Empty Vague Set
2.5. Similarity Measures of Vague Sets
2.6. Vague Relations
2.7. Operations on Vague Sets
3. Hesitation Information
3.1. Modeling Hesitation Status
3.2. Hesitation and Overall Hesitation
3.3. Intent and Overall Intent
3.4. Attractiveness and Overall Attractiveness
3.5. Attractiveness Hesitation Pair Database
4. Association Rules
4.1. Traditional Association Rule Mining
4.2. Vague Association Rule
4.3. Support of Vague Associations Rule
4.4. Confidence of Vague Association Rule
5. Mining Vague Association Rule
6. Background and Related Work
7. Discussions
8. Conclusions
References