A three-way decision method based on hybrid data1

An information system as a database that represents relationships between objects and attributes is an important mathematical model in the field of artificial intelligence. Hybrid data means boolean, categorical, real-valued, set-valued data and missing data in this paper. A hybrid information system is an information system where its attribute is hybrid data. This paper proposes a three-way decision method based on hybrid data. First, the distance between two objects based on the conditional attribute set in a given hybrid information system is developed and Gaussian kernel based on this distance is acquired. Then, the fuzzy Tcos-equivalence relation, induced by this information system, is obtained by using Gaussian kernel. Next, the decision-theoretic rough set model in this hybrid information system is presented. Moreover, a three-way decision method is given by means of this decision-theoretic rough set model and inclusion degree between two fuzzy sets. Finally, an example is employed to illustrate the feasibility of the proposed method, which may provide an effective method for hybrid data analysis in real applications.

Mining communities and their descriptions on attributed graphs: a survey

Finding communities that are not only relatively densely connected in a graph but that also show similar characteristics based on attribute information has drawn strong attention in the last years. There exists already a remarkable body of work that attempts to find communities in vertex-attributed graphs that are relatively homogeneous with respect to attribute values. Yet, it is scattered through different research fields and most of those publications fail to make the connection. In this paper, we identify important characteristics of the different approaches and place them into three broad categories: those that select descriptive attributes, related to clustering approaches, those that enumerate attribute-value combinations, related to pattern mining techniques, and those that identify conditional attribute weights, allowing for post-processing. We point out that the large majority of these techniques treat the same problem in terms of attribute representation, and are therefore interchangeable to a certain degree. In addition, different authors have found very similar algorithmic solutions to their respective problem.

Three-way decision for incomplete real-valued data

An information system (IS) is a database that expresses relationships between objects and attributes. An IS with decision attributes is said to be a decision information system (DIS). An incomplete real-valued decision information system (IRVDIS) is a DIS based on incomplete real-valued data. This paper studies three-way decision (3WD) for incomplete real-valued data and its application. In the first place, the distance between two objects on the basis of the conditional attribute set in an IRVDIS is constructed. In the next place, the fuzzy Tcos-equivalence relation on the object set of an IRVDIS is received by means of Gaussian kernel. After that, the decision-theoretic rough set model for an IRVDIS is presented. Furthermore, the 3WD method is proposed based on this model. Lastly, to illustrate the feasibility of the proposed method, an application of the proposed method is given. It is worth mentioning that levels of risk may be determined by thresholds that can be directly acquired according to risk preference of different decision-makers, as well as the decision rule for each decision class under different levels of risk is showed in tabular forms.

Attribute reduction of relative knowledge granularity in intuitionistic fuzzy ordered decision table

For the moment, the attribute reduction algorithm of relative knowledge granularity is very important research areas. It provides a new viewpoint to simplify feature set. Based on the decision information is unchanged, fast and accurate deletion of redundant attributes, which is the meaning of attribute reduction. Distinguishing ability of attribute sets can be well described by relative knowledge granularity in domain. Therefore, how to use the information based on relative knowledge granularity to simplify the calculation of attribute reduction. It is an important direction of research. For increasing productiveness and accuracy of attribute reduction, in this paper we investigate attribute reduction method of relative knowledge granularity in intuitionistic fuzzy ordered decision table(IFODT). More precisely, we redefine the granularity of knowledge and the relative knowledge granularity by ordered relation. And their relevant properties are proved. On the premise that the decision results remain unchanged, in order to accurately calculate the relative importance of any condition attributes about the decision attribute sets, the conditional attribute of internal and external significance are designed by relative knowledge granularity. And some important properties of relative attribute significance are proved. Therefore, we determine the importance of conditional attributes based on the size of the relative attribute significance. In the aspect of computation, the corresponding algorithm is designed and time complexity of algorithm is calculated. Moreover, the attribute reduction model of relative knowledge granularity of efficiency and accuracy is proved by test. Last, the validity of algorithm is demonstrated by an case about IFODT.

An Improved Value Reduction Algorithm Based on Core Value

Value reduction algorithm can filter and delete redundant conditional attribute value, so as to obtain decision rule of information system with least conditional attribute values. Based on the introduction of basic value reduction algorithm, the paper supplemented functions. Aiming at the circumstance of there is no repeated record and no conflict after deleting some attributes of a record, the algorithm supplement it. The example of value reduction based on the improved algorithm illustrated that it is an effective value reduction algorithm and an important supplement of basic value reduction algorithm.