Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks, in these days, require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This, in turn, imposes severe computational requirements on the relevant clustering techniques. A family of bio-inspired algorithms, well-known as Swarm Intelligence (SI) has recently emerged that meets these requirements and has successfully been applied to a number of real world clustering problems. This chapter explores the role of SI in clustering different kinds of datasets. It finally describes a new SI technique for partitioning any dataset into an optimal number of groups through one run of optimization. Computer simulations undertaken in this research have also been provided to demonstrate the effectiveness of the proposed algorithm.

Abstract. We introduce a methodology to improve Adaptive Systems for Web-Based Education. This methodology uses evolutionary algorithms as a data mining method for discovering interesting relationships in students ’ usage data. Such knowledge may be very useful for teachers and course authors to select the most appropriate modifications to improve the effectiveness of the course. We use Grammar-Based Genetic Programming (GBGP) with multi-objective optimization techniques to discover prediction rules. We present a specific data mining tool that can help non-experts in data mining carry out the complete rule discovery process, and demonstrate its utility by applying it to an adaptive Linux course that we developed. Key words. adaptive system for web-based education, data mining, evolutionary algorithms, grammar-based genetic programming, prediction rules

While being it extremely important, many Exploratory Data Analysis (EDA [21]) systems have the inhability to perform classification and visualization in a continuous basis or to self-organize new data-items into the older ones (evenmore into new labels if necessary), which can be crucial in KDD - Knowledge Discovery [10,1], Retrieval and Data Mining Systems [15,10] (interactive and online forms of Web Applications are just one example). This disadvantge is also present in more recent approaches using Self-Organizing Maps [4,22].

This paper reviews three recent books on data mining written from three different perspectives, i.e. databases, machine learning, and statistics. Although the exploration in this paper is suggestive instead of conclusive, it reveals that besides some common properties, different perspectives lay strong emphases on different aspects of data mining. The emphasis of the database perspective is on efficiency because this perspective strongly concerns the whole discovery process and huge data volume. The emphasis of the machine learning perspective is on effectiveness because this perspective is heavily attracted by substantive heuristics working well in data analysis although they may not always be useful. As for the statistics perspective, its emphasis is on validity because this perspective cares much for mathematical soundness behind mining methods.

Abstract. Self-Organizing Maps (SOM) are very powerful tools for data mining, in particular for visualizing the distribution of the data in very highdimensional data sets. Moreover, the 2D map produced by SOM can be used for unsupervised partitioning of the original data set into categories, provided that this map is somehow adequately segmented in clusters. This is usually done either manually by visual inspection, or by applying a classical clustering technique (such as agglomerative clustering) to the set of prototypes corresponding to the map. In this paper, we present a new approach for the segmentation of Self-Organizing Maps after training, which is both very simple and efficient. Our algorithm is based on a post-processing of the U-matrix (the matrix of distances between adjacent map units), which is directly derived from an elementary image-processing technique. It is shown on some simulated data sets that our partitioning algorithm appears to give very good results in terms of segmentation quality. Preliminary results on a real data set also seem to indicate that our algorithm can produce meaningful clusters on real data. 1.

Fuzzy sets constitute the oldest and most reported soft computing paradigm. They are well-suited to modeling different forms of uncertainties and ambiguities, often encountered in real life. Integration of fuzzy sets with other soft computing tools has lead to the generation of more powerful, intelligent and efficient systems. In this position paper we seek to outline the contribution offuzzy sets to pattern recognition, image processing, and machine intelligence over the last 40 years.

Abstract—A methodology is described for evolving a Rough-fuzzy multi layer perceptron with modular concept using a genetic algorithm to obtain a structured network suitable for both classification and rule extraction. The modular concept, based on “divide and conquer ” strategy, provides accelerated training and a compact network suitable for generating a minimum number of rules with high certainty values. The concept of variable mutation operator is introduced for preserving the localized structure of the constituting knowledge-based subnetworks, while they are integrated and evolved. Rough set dependency rules are generated directly from the real valued attribute table containing fuzzy membership values. Two new indices viz., “certainty ” and “confusion ” in a decision are defined for evaluating quantitatively the quality of rules. The effectiveness of the model and the rule extraction algorithm is extensively demonstrated through experiments alongwith comparisons. Index Terms—Soft computing, knowledge-based fuzzy networks, rough sets, genetic algorithms, pattern recognition, rule extraction/ evaluation, knowledge discovery, data mining. æ 1

Most current tools for data mining lack support for intelligent analysis and filtering of mined patterns. Dividing interesting mining results from uninteresting ones still is a laborious task mainly performed by human users. We propose to employ formalized domain knowledge for assessing the interestingness of mining results. We present considerations and ideas as foundations of the design of an intelligent data mining environment.

Algorithms based on systematic search techniques can be successfully applied for multilayer perceptron (MLP) training and for logical rule extraction from data using MLP networks. The proposed solutions are easier to implement and frequently outperform gradient-based optimization algorithms. Search-based techniques, popular in artificial intelligence and almost completely neglected in neural networks can be the basis for MLP network training algorithms. There are plenty of well-known search algorithms, however since they are not suitable for MLP training, new algorithms dedicated to this task must be developed. Search algorithms applied to MLP networks change network parameters (weights and biases) and check the influence of the changes on the error function. MLP networks considered in this thesis are used for data classification and logical rule-based understanding of the data. The proposed solutions in many cases outperform gradient-based backpropagation algorithms. The thesis is organized in three parts. The first part of the thesis concentrates on better understanding of MLP properties.

This paper presents a study of knowledge based descriptive neural networks (DNN). DNN is a neural network that incorporates rules extracted from trained neural networks. One of the major drawbacks of neural network models is that they could not explain what they have done.