We formulate linguistic rule extraction as a three-objective combinatorial optimization problem. Three objectives are to maximize the performance of an extracted rule set, to minimize the number of extracted rules, and to minimize the total length of extracted rules. The second and third objectives are related to comprehensibility of the extracted rule set. We describe and compare two genetic-a...

Association rule mining (ARM) aims at extraction, hidden relation, and interesting associations between the existing items in a transactional database. The purpose of this study is to highlight fundamental of association rule mining, association rule mining approaches to mining association rule, various algorithm and comparison between algorithms. Keywords—Assocation Rule Mining; Bottom up Appr...

Today network security, uptime and performance of network are important and serious issue in computer network. Anomaly is deviation from normal behavior which is factor that affects on network security. So Anomaly Extraction which detects and extracts anomalous flow from network is requirement of network operator. Anomaly extraction refers to automatically finding in a large set of flows observ...

A method of extracting intuitive knowledge from neural network classifiers is presented in the paper. An algorithm which obtains crisp rules in the form of logical implications which approximately describe the neural network mapping is introduced. The number of extracted rules can be selected using an uncertainty margin parameter as well as by changing the precision level of the soft quantizati...

Abstract. Generalized relevance learning vector quantization (GRLVQ) [4] constitutes a prototype based clustering algorithm based on LVQ [5] with energy function and adaptive metric. We propose a method for extracting logical rules from a trained GRLVQ-network. Real valued attributes are automatically transformed to symbolic values. The rules are given in the form of a decision tree yielding se...

A coevolutionary algorithm called reCORE for rule extraction from databases was proposed in the paper. Knowledge is extracted in the form of simple implication rules IF-THEN-ELSE. There are two populations involved, one for rules and second for rule sets. Each population has a distinct evolution scheme. One individual from rule set population and a number of individuals from rule population con...

This thesis investigates rule extraction from recurrent neural networks, which takes the form of automated construction of models of an underlying network. Typically the models are expressed as finite state machines and they should mimic the network while being more intelligible. It is argued that rule extraction allows a deeper and more general form of analysis than other, more or less ad hoc,...

Data projection is an important tool in exploratory data analysis. Sammon’s non linear projection method lacks predictability and is ineffective for large data sets. To introduce predictability we implement an extension of Sammon’s algorithm using fuzzy logic approach. The fuzzy based rule model is implemented in the .Net framework using Microsoft Visual Studio with Visual C# as the programming...

Instead of clustering video shots into scenes using low level image features, in this paper, we propose a rule-based model to extract simple dialog or action scenes. Through analyzing video editing rules and observing temporal appearance patterns of shots in dialog scenes of movies, we deduce a set of rules to recognize dialog or action scenes. Based on these rules, a finite state machine is de...

The present paper introduces a new destructive algorithm for rule extraction based on a trained neural network. The degree of complexity of neural network increases exponentially as a factor of the numbers of input and hidden nodes. Therefore, the dimensionality of the trained neural network is reduced by using a proposed destructive algorithm to extract only the most effective values of the in...