A self-adaptive migration model genetic algorithm for data mining applications

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others. © 2007 Elsevier Inc. All rights reserved

Comparative Analysis of Velocity Measurements In Ducted Axial Fan

The paper deals with experimental investigation and comparative analysis of velocity measurements in ducted axial fan. Experiments were carried out to investigate the nature of velocity variations in a ducted axial fan at different throttle positions as a function of rotor speed employing both Pitot tube and Hot Wire Anemometer. Quantitative analyses of the magnitudes of velocity measured by a pitot tube as well as a hot wire anemometer are examined and various graphs have been plotted. The percentage errors of velocity level have been determined

SAGAXsearch: An XML Information Retrieval Mechanism: AN XML INFORMATION RETRIEVAL MECHANISM USING SELF ADAPTIVE GENETIC ALGORITHMS

The XML technology, with its self-describing and extensible tags, is significantly contributing to the next generation semantic web. The present search techniques used for HTML and text documents are not efficient when retrieving relevant XML documents. In this paper, Self Adaptive Genetic Algorithms are presented to learn about the tags, which are useful in indexing. The indices and relationship strength metric are used to extract fast and accurate semantically related elements in the XML documents. The Experiments are conducted on the DataBase systems and Logic Programming (DBLP) XML corpus and are evaluated for precision and recall. The proposed SAGAXsearch outperforms XSEarch3 and XRank20 with respect to accuracy and query execution time

SOUND SPECTRUM MEASUREMENTS IN DUCTED AXIAL FAN UNDER STABLE CONDITION AT FREQUENCY RANGE 6000 TO 6600 HZ

Performance of axial fan is found to reduce drastically when instability is encountered during its operation. Performance of an axial fan is severely impaired by many factors mostly related to system instabilities due to rotating stall and surge phenomenon experienced during its operation. The present work involves measuring the sound spectrum measurements in ducted axial fan under stable condition at frequency range from 6000 to 6600 Hz. Objective of the experiment is to measure the frequency domain signal and study the sound Characteristics in ducted axial fan by using spectrum analyser. Different types of FFT signals have been measured under stable condition for the frequency range of 6000 Hz to 6600 Hz with respect to rotor speed and different graphs are plotted for ducted axial fan

A Hybrid Approach to Cognition in Radars

In many engineering domains, cognition is emerging to play vital role. Cognition will play crucial role in radar engineering as well for the development of next generation radars. In this paper, a cognitive architecture for radars is introduced, based on hybrid cognitive architectures. The paper proposes deep learning applications for integrated target classification based on high-resolution radar range profile measurements and target revisit time calculation as case studies. The proposed architecture is based on the artificial cognitive systems concepts and provides a basis for addressing cognition in radars, which is inadequately explored for radar systems. Initial experimental studies on the applicability of deep learning techniques under this approach provided promising results

Feature extraction using fuzzy c-means clustering for data mining systems

Knowledge Discovery and Data Mining (KDD) process includes preprocessing, transformation, data mining and knowledge extraction. The two important tasks of data mining are clustering and classification. In this paper, we propose a generic feature extraction for classification using Fuzzy C-Means (FCM) clustering. The raw data is preprocessed, normalized and then data points are clustered using fuzzy c-means technique. Feature vectors for all the classes are generated by extracting the most relevant features from the corresponding clusters and used for further classification. Artificial Neural Network and Support Vector Machines are used to perform the classification task. Experiments are conducted on four datasets and the accuracy obtained by performing specific feature extraction for a particular data set is compared with generic feature extraction scheme. The algorithm performs relatively well with respect to classification results when compared with the specific feature extraction technique

Front Matter - Soft Computing for Data Mining Applications

Efficient tools and algorithms for knowledge discovery in large data sets have been devised during the recent years. These methods exploit the capability of computers to search huge amounts of data in a fast and effective manner. However, the data to be analyzed is imprecise and afflicted with uncertainty. In the case of heterogeneous data sources such as text, audio and video, the data might moreover be ambiguous and partly conflicting. Besides, patterns and relationships of interest are usually vague and approximate. Thus, in order to make the information mining process more robust or say, human-like methods for searching and learning it requires tolerance towards imprecision, uncertainty and exceptions. Thus, they have approximate reasoning capabilities and are capable of handling partial truth. Properties of the aforementioned kind are typical soft computing. Soft computing techniques like Genetic

Effects of Information Filters: A Phenomenon on the Web

In the Internet era, information processing for personalization and relevance has been one of the key topics of research and development. It ranges from design of applications like search engines, web crawlers, learning engines to reverse image searches, audio processed search, auto complete, etc. Information retrieval plays a vital role in most of the above mentioned applications. A part of information retrieval which deals with personalization and rendering is often referred to as Information Filtering. The emphasis of this paper is to empirically analyze the information filters commonly seen and to analyze their correctness and effects. The measure of correctness is not in terms of percentage of correct results but instead a rational approach of analysis using a non mathematical argument is presented. Filters employed by Google’s search engine are used to analyse the effects of filtering on the web. A plausible

Sagaxsearch: An XML Information Retrieval Mechanism Using Self Adaptive Genetic Algorithms

The XML technology, with its self-describing and extensible tags, is significantly contributing to the next generation semantic web. The present search techniques used for HTML and text documents are not efficient when retrieving relevant XML documents. In this paper, Self Adaptive Genetic Algorithms are presented to learn about the tags, which are useful in indexing. The indices and relationship strength metric are used to extract fast and accurate semantically related elements in the XML documents. The Experiments are conducted on the DataBase systems and Logic Programming (DBLP) XML corpus and are evaluated for precision and recall. The proposed SAGAXsearch outperforms XSEarch3 and XRank20 with respect to accuracy and query execution time

Generic Feature Extraction for Classification using Fuzzy C - Means Clustering

Knowledge discovery and data mining (KDD) process includes preprocessing, transformation, data mining and knowledge extraction. The two important tasks of data mining are clustering and classification. In this paper, we propose a generic feature extraction for classification using fuzzy C-means (FCM) clustering. The raw data is preprocessed, normalized and then data points are clustered using the fuzzy C-means technique. Feature vectors for all the classes are generated by extracting the most relevant features from the corresponding clusters and used for further classification. Artificial neural network and support vector machines are used to perform the classification task. Experiments are conducted on four datasets and the accuracy obtained by performing specific feature extraction for a particular data set is compared with the generic feature extraction scheme. The algorithm performs relatively well with respect to classification results when compared with the specific feature extraction techniqu