Fuzzy Sets in Information Retrieval and Cluster Analysis

Knowledge in Flux

This study surveys the ordered weighted averaging OWA operator literature using a citation network analysis. The main goals are the historical reconstruction of scientific development of the OWA field, the identification of the dominant direction of knowledge accumulation that emerged since the publication of the first OWA paper, and to discover the most active lines of research. The results suggest, as expected, that Yager's paperIEEE Trans. Systems Man Cybernet, 181, 183-190, 1988 is the most influential paper and the starting point of all other research using OWA. Starting from his contribution, other lines of research developed and we describe them.

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Ordered Weighted Averaging Operators 1988-2014: A Citation-Based Literature Survey

A fuzzy document retrieval system using the keyword connection matrix and a learning method

The rise of e-commerce has not only given consumers more choice but has also caused information overload. In order to quickly find favorite items from vast resources, users are eager for technology by which websites can automatically deliver items in which they may be interested. Thus, recommender systems are created and developed to automate the recommendation process. In the field of collaborative filtering recommendations, the accuracy requirement of the recommendation algorithm always makes it complex and difficult to implement one algorithm. The slope one algorithm is not only easy to implement but also works efficient and effective. However, the prediction accuracy of the slope one algorithm is not very high. Moreover, the slope one algorithm does not perform so well when dealing with personalized recommendation tasks that concern the relationship among users. To solve these problems, we propose a slope one algorithm based on the fusion of trusted data and user similarity, which can be deployed in various recommender systems. This algorithm comprises three procedures. First, we should select trusted data. Second, we should calculate the similarity between users. Third, we need to add this similarity to the weight factor of the improved slope one algorithm, and then, we get the final recommendation equation. We have carried out a number of experiments with the Amazon dataset, and the results prove that our recommender algorithm performs more accurately than the traditional slope one algorithm.

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A trust-based collaborative filtering algorithm for E-commerce recommendation system

This paper deals with multi-criteria decision making (MCDM) problems with multiple priorities, in which priority weights associated with the lower priority criteria are related to the satisfactions of the higher priority criteria. Firstly, we propose a prioritized weighted aggregation operator based on ordered weighted averaging (OWA) operator and triangular norms (t-norms). To preserve the tradeoffs among the criteria in the same priority level, we suggest that the degree of satisfaction regarding each priority level is viewed as a pseudo criterion. On the other hand, t-norms are used to model the priority relationships between the criteria in different priority levels. In particular, we show that strict Archimedean t-norms perform better in inducing priority weights. As Hamacher family of t-norms provide a wide class of strict Archimedean t-norms ranging from the product to weakest t-norm, Hamacher parameterized t-norms are used to induce the priority weight for each priority level. Secondly, considering decision maker (DM)'s requirement toward higher priority levels, a benchmark based approach is proposed to induce priority weight for each priority level. In particular, Lukasiewicz implication is used to compute benchmark achievement for crisp requirements; target-oriented decision analysis is utilized to obtain the benchmark achievement for fuzzy requirements. Finally, some numerical examples are used to illustrate the proposed prioritized aggregation technique as well as to compare with previous research.

On prioritized weighted aggregation in multi-criteria decision making

A fuzzy document retrieval method based on two-valued indexing

The new notion of semantic fields for modal logic is introduced, by which a degree of significance can be assigned to any possible world. Available information is the main source of the significance that shows the logical structure of the information itself. Thus, a guide is obtained to use information and also to discover and measure the uncertainty located in it. The worlds are useful for separating such information into its important parts. Any sentence is evaluated in worlds that are located at different levels of significance. An accessibility relation exists between the more significant and the less significant world. In this way, possible-worlds models of information are made possible using the semantic field. Uncertainty is given, normally, without any logic but with set theory. Thus, the semantic field is used to connect modal logic with set theory in order to obtain a better description of uncertainty.

Field theory and modal logic by semantic fields to make uncertainty emerge from information

A fuzzy-measure-based approach to semantics for modal logic is presented and its several properties are discussed. Measure-based models for modal logic are defined and the soundness and completeness theorems of several systems of modal logic are proved with respect to classes of finite measure-based models, particularly, formulated by fuzzy, possibility, necessity, probability, and Dirac measures.

Tetsuya Murai

Papers

On prioritized weighted aggregation in multi-criteria decision making

A fuzzy document retrieval method based on two-valued indexing

Field theory and modal logic by semantic fields to make uncertainty emerge from information

Measure-Based Semantics for Modal Logic

Granular Reasoning Using Zooming In & Out