Relational database management systems manages the tables with predefined indexes. In RDBMS indexes are created on the basis of attribute values. The Imine indexing scheme is used to index item sets in relational databases. The index creation is performed with out any constraints. IMine provides a complete representation of the original database. The Imine indexing scheme reduces the input/outp...

Today’s world economy situation forces enterprise organizations toward more soft and flexible organization, management, and production processes. They need to explore the most suitable Knowledge Management (KM) tool not only to identify gaps and overlaps but also to maintain and support innovation cross organizations. In this study, a multiple-experts-multiple-criteria decision making model is ...

In this study we suggest a new group decision-making method which is based on some basic fuzzy set operations. By using this method, we can get two types of results. First, we can identify which alternative is the best. The second result, a crucial point of this work, is the screening of decisionmakers. The decision-makers should be serious and responsible in giving their opinions otherwise the...

Most decision support systems based on rough set theory are related to the minimal reduct calculation problem, which is NP-hard. This paper investigates the problem of searching for the set of useful attributes that occur in at least one reduct. By compliment, this problem is equivalent to searching for the set of redundant attributes, i.e. the attributes that do not occur in any reducts of the...

In one-class classification, the problem is to distinguish one class of data from the rest of the feature space. It is important in many applications where one of the classes is characterized well, while no measurements are available for the other class. Schölkopf et al. first introduced a method of adapting the support vector machine (SVM) methodology to the one-class classification problem, c...

We present a learning algorithm for decision lists which allows features that are constructed from the data and allows a trade-off between accuracy and complexity. We provide bounds on the generalization error of this learning algorithm in terms of the number of errors and the size of the classifier it finds on the training data. We also compare its performance on some natural data sets with th...

With respect to different confidence on evaluation result from different decision-makers to the hybrid multi-attribute with risk, an approach to group decisionmaking based on prospect theory and projection theory is proposed. Firstly, a tuple is established to record the evaluation result and the hybrid decision information. Then the element of the tuple is changed into a single triangular fuzz...

A novel decision support framework has been proposed herein to solve supplier selection problems by considering green as well as resiliency criteria, simultaneously. In this work subjectivity of evaluation criteria has been tackled by exploring fuzzy set theory. A satisfaction function and distance based approach has been conceptualized in this work to operate under fuzzy environment. Applicati...

The rough set theory approximates a concept by three regions, namely, the positive, boundary and negative regions. Rules constructed from the three regions are associated with different actions and decisions, which immediately leads to the notion of three-way decision rules. A positive rule makes a decision of acceptance, a negative rule makes a decision of rejection, and a boundary rule makes ...

A recognition system based on fuzzy set theory and approximate reasoning has been described that is capable of handling various imprecise input patterns and providing a natural decision system. The input feature is considered to be of either quantative form or linguistic form or mixed form or set form. The entire feature space is decomposed here into some overlapping subdomains depending on the...