Fuzzy Logic in Data Mining

Data mining comprises meticulous theories and techniques from vast areas of knowledge on how to extract large volumes of data. However, uncertainty is a global problem in data mining. The ongoing challenges of uncertainty give rise to a plethora of knowledge extracting methods that use fuzzy logic. The internet is overwhelmingly full of information; meanwhile, every choice we make is recorded. All of these are just personal choices. Data mining is mainly focused on solving problems by analyzing data already present in databases. Suppose to take a well-worn example; the problem is fickle customer loyalty in a highly competitive marketplace. A database of customer choices, along with customer profiles, holds the key to this problem. Data mining system using pattern and Fuzzy logic technique is a tool that situates the primary rules for a specific desired output.  Fuzzy logic can be defined as a multiple-valued logic form in which we may get actual variable values in any integers between 0 and 1.

It can be said as it is the handle concept of truth in parts. In reality, we often crossroads where we fail to decide whether or not the statement is true. Now, fuzzy logic offers precious flexibility for reasoning. Fuzzy logic is used to deal with imprecise information. Data mining comprises methods and classifications. These are discussed for both precise and imprecise information. In data mining, retrieval of information is important. In big data, time and space complexity is high. Data mining, fuzzy data mining, and web data mining are discussed through MapReduce algorithms. MapReduce is a programming model which permits the simple development of noticeable parallel applications with which process big data on huge clusters of commodity machines. The Fuzzy Logic Systems is of easy and understandable construction. Fuzzy logic comes with quite simple mathematical concepts of set theory and reasoning. This system can work with almost any type of inputs, whether it is inexact, distorted, or noisy input information. The algorithms can be described with minimal data, so little memory is required. It is used in the field of aerospace for monitoring of altitude control of spacecraft & satellites. It is also used in the sector of the automotive system for the management of speed, control of traffic. It can also use in decision-making support systems and evaluation of personnel in large businesses. It can be applied for controlling the pH, drying, chemical distillation process in the chemical industry. It is also used in Natural language processing(NLP) and various intensive applications in Artificial Intelligence (AI).

Fuzzy logic is used in modern control systems such as expert systems. Fuzzy logic is used with Neural Networks as it mimics how a person would make decisions, only much faster. It is done by aggregation of data and changing into more meaningful data by forming partial truths as Fuzzy sets. Fuzzy logic can also be used in real-life situations where decisions are made on the basis of multiple interlinked criteria. The same situation was also viable for the aspect-based opinion classification process, for which case algorithm takes the decision the class/label of opinions with multiple aspects and opinion words. For instance, in a hotel review, a reviewer praises the decoration of the hotel, but he also disapproves of the service provided by the hotel staff. Therefore, the decision about the opinion label, either positive or negative, relies on the opinion words or phrases, or parts of sentences used by the reviewer for each of the criteria. Experiments were conducted on real-world hotels and restaurant reviews taken from websites like OpenTable and TripAdvisor. To tabulate the overall performance and effectiveness of fuzzy aspects rooted in the opinion classification system, it examined the effects of dataset feature size, size, time, feature types, and feature weighted methods on the performance. It is a fact that, for instance, the opinion classification process where the algorithms need to understand the opinion expressed by a visitor in a review based on the views about various aspects of the tourist place. For example, in restaurant reviews, some reviewers may praise the restaurant’s decoration and some blame the service along with the staff. Deciding on the opinion as positive or negative depends on the opinion words or phrases used by the reviewers for each aspect. When the number of elements is more, the complexity in the decision-making gets added, and hence the decision-making becomes tough in such situations, where fuzzy logic can be effectively used. Fuzzy logic helps to remove the repeated data, which can delay the process of analyzing the data. There are more advantages of using Fuzzy logic in data mining. The results will show that Fuzzy logic can accurately identify the data on what the user wants—searching for patterns of interest in a unique representation form or a set of such representations: classification rules or trees, regression. Some CI models lend themselves to transform into other model structures that allow information transfer between different models.