Unexpected rules using a conceptual distance based on fuzzy ontology

AbstractOne of the major drawbacks of data mining methods is that they generate a notably large number of rules that are often obvious or useless or, occasionally, out of the user’s interest. To address such drawbacks, we propose in this paper an approach that detects a set of unexpected rules in a discovered association rule set. Generally speaking, the proposed approach investigates the discovered association rules using the user’s domain knowledge, which is represented by a fuzzy domain ontology. Next, we rank the discovered rules according to the conceptual distances of the rules

A Fuzzy-Mining Approach for Solving Rule Based Expert System Unwieldiness in Medical Domain

Over the years, one of the challenges of a rule based expert system is the possibility of evolving a compact and consistent knowledge-base with a fewer numbers of rules that are relevant to the application domain, in order to enhance the comprehensibility of the expert system. In this paper, the hybrid of fuzzy rule mining interestingness measures and fuzzy expert system is exploited as a means of solving the problem of unwieldiness and maintenance complication in the rule based expert system. This negatively increases the knowledge-base space complexity and reduces rule access rate which impedes system response time. To validate this concept, the Coronary Heart Disease risk ratio determination is used as the case study. Results of fuzzy expert system with a fewer numbers of rules and fuzzy expert system with a large numbers of rules are presented for comparison. Moreover, the effect of fuzzy linguistic variable risk ratio is investigated. This makes the expert system recommendation close to human perception

Data mining in soft computing framework: a survey

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included

Comparison of deposition methods of ZnO thin film on flexible substrate

This paper reports the effect of the different deposition methods towards the ZnO nanostructure crystal quality and film thickness on the polyimide substrate. The ZnO film has been deposited by using the spray pyrolysis technique, sol-gel and RF Sputtering. Different methods give a different nanostructure of the ZnO thin film. Sol gel methods, results of nanoflowers ZnO thin film with the thickness of thin film is 600nm. It also produces the best of the piezoelectric effect in term of electrical performance, which is 5.0 V and 12 MHz of frequency which is higher than other frequency obtained by spray pyrolysis and RF sputtering

Study on intrusion detecton using average matching degree space based on class association rule mining

制度:新 ; 報告番号:甲3767号 ; 学位の種類:博士(工学) ; 授与年月日:2013/1/28 ; 早大学位記番号:新6140Waseda Universit