Using Basic Rough Set Theory Concepts Marketing Essay
The paper describes a knowledge discovery paradigm for multi-attribute, multi-criterion decision making, which is based on the concept of rough sets; This chapter discusses the basic preparations for rough set theory RST. Since its inception, RST has been a prominent data analytics tool because of its analysis. Rough set theory has been a methodology for database mining or knowledge discovery in relational databases. In its abstract form it is a new area of uncertainty, Introduction. Marketing management is a business discipline that focuses on marketing techniques and the management of a company's marketing resources and activities; Introduction. Intelligent information processing is a hot issue in theory and application research in computer science. Due to the development of Rough Set Theory, it is a tool for dealing with granularity and vagueness in data analysis. The crude method has already been applied in various fields, such as process control, economics and medicine. The RoughSets package, written primarily in the R language, provides implementations of methods from the rough set theory RST and the fuzzy rough set theory FRST for data modeling and analysis. It takes into account not only fundamental concepts such as indiscernibility relations, bottom-up approaches, etc., but also their applications. In this article we present a comparative study of rough set theory and the analysis of formal concepts. The two theories pursue different goals and summarize. different types of knowledge. Rough set. The marketing mix is a term that describes the multifaceted approach to a complete and effective marketing plan. Traditionally, this plan included the four P's of marketing: price, product, promotion and place. But the exact composition of a marketing mix has undergone several changes in response to new technologies and ways of thinking. This chapter emphasizes the role that rough set theory RST plays within the broad field of Machine Learning ML. As a solid data analysis and knowledge discovery paradigm, RST has a lot to offer the ML community. We have surveyed the existing literature and reported on the most relevant RST theoretical developments and rough set theory and its applications. The first principles of the theory will be outlined, and the basic concepts of the theory will be illustrated through a simple instructional example, relating to churn modeling in telecommunications, and it will be considered as an independent, complementary, non- competitive discipline in itself. Expand.This article presents basic concepts and various areas of research in rough set theory. Rough set theory is a new mathematical approach to imperfect knowledge. The problem of imperfect knowledge has long been addressed by philosophers, logicians, and mathematicians. One of the most important concepts in gray systems theory is how to control systems in an incomplete or lack of information situation. A gray number indicating an uncertain value is described in real interval from this concept. In this paper, we introduce the real formal concept analysis based on gray-rough set theory by using gray, implementations of data analysis algorithms based on the rough set theory RST and the fuzzy rough set theory FRST. We provide not only implementations for the basic concepts of RST and FRST, but also popular algorithms that emerge from these theories. The methods included in the package can be divided into,