Mamdani’s fuzzy inference method is the most commonly seen fuzzy methodology. It was proposed in 1975 by Ebrahim Mamdani as an attempt to control a steam engine and boiler combination. Mamdani-type inference expects the output membership functions to be fuzzy sets. After the aggregation process, there is a fuzzy set for each output variable that needs defuzzification. It is possible, and in many cases much more efficient, to use a single spike as the output membership function rather than a distributed fuzzy set. This type of output is sometimes known as a singleton output membership function, and it can be thought of as a “predefuzzified” fuzzy set. It enhances the efficiency of the defuzzification process because it greatly simplifies the computation required by the more general Mamdani method, which finds the centroid of a two-dimensional function. Rather than integrating across the two-dimensional function to find the centroid, you use the weighted average of a few data points. Sugeno-type systems support this type of model.
Published in Chapter:
A Machine Learning Approach to Data Cleaning in Databases and Data Warehouses
Hamid Haidarian Shahri (University of Maryland, USA)
Copyright: © 2008
|Pages: 15
DOI: 10.4018/978-1-59904-853-6.ch030
Abstract
Entity resolution (also known as duplicate elimination) is an important part of the data cleaning process, especially in data integration and warehousing, where data are gathered from distributed and inconsistent sources. Learnable string similarity measures are an active area of research in the entity resolution problem. Our proposed framework builds upon our earlier work on entity resolution, in which fuzzy rules and membership functions are defined by the user. Here, we exploit neuro-fuzzy modeling for the first time to produce a unique adaptive framework for entity resolution, which automatically learns and adapts to the specific notion of similarity at a meta-level. This framework encompasses many of the previous work on trainable and domain-specific similarity measures. Employing fuzzy inference, it removes the repetitive task of hard-coding a program based on a schema, which is usually required in previous approaches. In addition, our extensible framework is very flexible for the end user. Hence, it can be utilized in the production of an intelligent tool to increase the quality and accuracy of data.