Selection of Prototype Rules: Context Searching Via Clustering
نویسندگان
چکیده
Prototype-based rules are an interesting alternative to fuzzy and crisp logical rules, in many cases providing simpler, more accurate and more comprehensible description of the data. Such rules may be directly converted to fuzzy rules. A new algorithm for generation of prototype-based rules is introduced and a comparison with results obtained by neurofuzzy systems on a number of datasets provided.
منابع مشابه
Developing a Course Recommender by Combining Clustering and Fuzzy Association Rules
Each semester, students go through the process of selecting appropriate courses. It is difficult to find information about each course and ultimately make decisions. The objective of this paper is to design a course recommender model which takes student characteristics into account to recommend appropriate courses. The model uses clustering to identify students with similar interests and skills...
متن کاملImproving Accuracy of LVQ Algorithm by Instance Weighting
Similarity-based methods belong to the most accurate data mining approaches. A large group of such methods is based on instance selection and optimization, with Learning Vector Quantization (LVQ) algorithm being a prominent example. Accuracy of LVQ highly depends on proper initialization of prototypes and the optimization mechanism. Prototype initialization based on context dependent clustering...
متن کاملLVQ algorithm with instance weighting for generation of prototype-based rules
Crisp and fuzzy-logic rules are used for comprehensible representation of data, but rules based on similarity to prototypes are equally useful and much less known. Similarity-based methods belong to the most accurate data mining approaches. A large group of such methods is based on instance selection and optimization, with the Learning Vector Quantization (LVQ) algorithm being a prominent examp...
متن کاملApplying a decision support system for accident analysis by using data mining approach: A case study on one of the Iranian manufactures
Uncertain and stochastic states have been always taken into consideration in the fields of risk management and accident, like other fields of industrial engineering, and have made decision making difficult and complicated for managers in corrective action selection and control measure approach. In this research, huge data sets of the accidents of a manufacturing and industrial unit have been st...
متن کاملBuilding Localized Basis Function Networks Using Context Dependent Clustering
Networks based on basis set function expansions, such as the Radial Basis Function (RBF), or Separable Basis Function (SBF) networks, have non-linear parameters that are not trivial to optimize. Clustering techniques are frequently used to optimize positions for localized functions. Context-dependent fuzzy clustering techniques improve convergence of parameter optimization, leading to better ne...
متن کامل