Preference Mining Using Neighborhood Rough Set Model on Two Universes

Preference mining plays an important role in e-commerce and video websites for enhancing user satisfaction and loyalty. Some classical methods are not available for the cold-start problem when the user or the item is new. In this paper, we propose a new model, called parametric neighborhood rough set on two universes (NRSTU), to describe the user and item data structures. Furthermore, the neighborhood lower approximation operator is used for defining the preference rules. Then, we provide the means for recommending items to users by using these rules. Finally, we give an experimental example to show the details of NRSTU-based preference mining for cold-start problem. The parameters of the model are also discussed. The experimental results show that the proposed method presents an effective solution for preference mining. In particular, NRSTU improves the recommendation accuracy by about 19% compared to the traditional method

Covering rough sets based on neighborhoods: An approach without using neighborhoods

Rough set theory, a mathematical tool to deal with inexact or uncertain knowledge in information systems, has originally described the indiscernibility of elements by equivalence relations. Covering rough sets are a natural extension of classical rough sets by relaxing the partitions arising from equivalence relations to coverings. Recently, some topological concepts such as neighborhood have been applied to covering rough sets. In this paper, we further investigate the covering rough sets based on neighborhoods by approximation operations. We show that the upper approximation based on neighborhoods can be defined equivalently without using neighborhoods. To analyze the coverings themselves, we introduce unary and composition operations on coverings. A notion of homomorphismis provided to relate two covering approximation spaces. We also examine the properties of approximations preserved by the operations and homomorphisms, respectively.Comment: 13 pages; to appear in International Journal of Approximate Reasonin

A Novel Rough Set Model in Generalized Single Valued Neutrosophic Approximation Spaces and Its Application

In this paper, we extend the rough set model on two different universes in intuitionistic fuzzy approximation spaces to a single-valued neutrosophic environment

Review and prioritization of investment projects in the Waste Management organization of Tabriz Municipality with a Rough Sets Theory approach

Purpose: Prioritization of investment projects is a key step in the process of planning the investment activities of organizations. Choosing the suitable projects has a direct impact on the profitability and other strategic goals of organizations. Factors affecting the prioritization of investment projects are complex and the use of traditional methods alone cannot be useful, so there is a need to use a suitable model for prioritizing projects and investment plans. The purpose of this study is to prioritize projects and investment methods for projects (10 projects) considered by the Waste Management Organization of Tabriz Municipality. Methodology: The method of analysis used is the theory of rough, so that first the important investment projects in the field of waste management were determined using the research background and opinion of experts and the weight and priority of the projects were obtained using the Rough Sets Theory. Then, the priority of appropriate investment methods (out of 6 methods) of each project was obtained using Rough numbers, the opinion of experts and other aspects. Findings: The result of the research has been that construction project of a specialized recycling town, plastic recycling project, and recycled tire recycling project are three priority projects of Tabriz Municipality Waste Management Organization, respectively. Three investment methods, civil partnership agreements, BOT, and BOO can be used for them. Originality/Value: Tabriz Municipality Waste Management is an important and influential organization in the activities of the city, in which the investment methods in its projects are mostly based on common contracts and are performed in the same way for all projects. This research offers new methods for projects and their diversity according to Rough Sets technique

Matroidal approaches to rough sets via closure operators

AbstractThis paper studies rough sets from the operator-oriented view by matroidal approaches. We firstly investigate some kinds of closure operators and conclude that the Pawlak upper approximation operator is just a topological and matroidal closure operator. Then we characterize the Pawlak upper approximation operator in terms of the closure operator in Pawlak matroids, which are first defined in this paper, and are generalized to fundamental matroids when partitions are generalized to coverings. A new covering-based rough set model is then proposed based on fundamental matroids and properties of this model are studied. Lastly, we refer to the abstract approximation space, whose original definition is modified to get a one-to-one correspondence between closure systems (operators) and concrete models of abstract approximation spaces. We finally examine the relations of four kinds of abstract approximation spaces, which correspond exactly to the relations of closure systems