K. G. Srinivasa

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.

Dynamic Association Rule Mining using Genetic Algorithms

A large volume of transaction data is generated everyday in a number of applications. These dynamic data sets have immense potential for reflecting changes in customer behaviour patterns. One of the strategies of data mining is association rule discovery which correlates the occurrence of certain attributes in the database leading to the identification of large data itemsets. This paper seeks to generate large itemsets in a dynamic transaction database using the principles of Genetic Algorithms. Intra Transactions, Inter Transactions and Distributed Transactions are considered for mining Association Rules. Further, we analyze the time complexities of single scan technique DMARG (Dynamic Mining of Association Rules using Genetic Algorithms), with Fast UPdate (FUP) algorithm for intra transactions and E-Apriori for inter transactions. Our study shows that the algorithm DMARG outperforms both FUP and E-Apriori in terms of execution time and scalability, without compromising the quality or completeness of rules generated.

Evolutionary approach for mining association rules on dynamic databases

A large volume of transaction data is generated everyday in a number of applications. These dynamic data sets have immense potential for reflecting changes in customer behaviour patterns. One of the strategies of data mining is association rule discovery, which correlates the occurrence of certain attributes in the database leading to the identification of large data itemsets. This paper seeks to generate large itemsets in a dynamic transaction database using the principles of Genetic Algorithms. Intra transactions, Inter transactions and distributed transactions are considered for mining association rules. Further, we analyze the time complexities of single scan DMARG(Dynamic Mining of Association Rules using Genetic Algorithms), with Fast UPdate (FUP) algorithm for intra transactions and E-Apriori for inter transactions. Our study shows that DMARG outperforms both FUP and E-Apriori in terms of execution time and scalability, without compromising the quality or completeness of rules generated. The problem of mining association rules in the distributed environment is explored in DDMARG(Distributed and Dynamic Mining of Association Rules using Genetic Algorithms).

A dynamic migration model for self-adaptive genetic algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. 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, when compared with Island model GA(IGA) and Simple GA(SGA).

Emerging Research Surrounding Power Consumption and Performance Issues in Utility Computing

Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher. ABSTRACT Face recognition is a sophisticated problem requiring a significant commitment of computer resources. A modern GPU architecture provides a practical platform for performing face recognition in real time. The majority of the calculations of an eigenpicture implementation of face recognition are matrix multiplications. For this type of computation, a conventional computer GPU is capable of computing in tens of milliseconds data that a CPU requires thousands of milliseconds to process. In this chapter, we outline and examine the different components and computational requirements of a face recognition scheme implementing the Viola-Jones Face Detection Framework and an eigenpicture face recognition model. Face recognition can be separated into three distinct parts: face detection, eigenvector projection , and database search. For each, we provide a detailed explanation of the exact process along with an analysis of the computational requirements and scalability of the operation.

GaXsearch: an XML information retrieval mechanism using 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 to retrieve relevant XML documents. In this paper, 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 GaXsearch outperforms XSEarch [1] and XRank [2] with respect to accuracy and query execution time.

Mining Top – k Ranked Webpages Using Simulated Annealing and Genetic Algorithms

Searching on the Internet has grown in importance over the last few years, as huge amount of information is invariably accumulated on the Web. The problem involves locating the desired information and corresponding URLs on the WWW. With billions of webpages in existence today, it is important to develop efficient means of locating the relevant webpages on a given topic. A single topic may have thousands of relevant pages of varying popularity. Top – k document retrieval systems identifies the top – k ranked webpages pertaining to a given topic. In this paper, we propose an efficient top-k document retrieval method (TkRSAGA), that works on the existing search engines using the combination of Simulated Annealing and Genetic Algorithms. The Simulated Annealing is used as an optimized search technique in locating the top-k relevant webpages, while Genetic Algorithms helps in faster convergence via parallelism. Simulations were conducted on real datasets and the results indicate that TkRSAGA outperforms the existing algorithms.

Indexing XML documents using self adaptive genetic algorithms for better retreival

The next generation of web is often characterized as the Semantic Web. Machines which are adept in processing data, will also perceive the semantics of the data. The XML technology, with its self describing and extensible tags, is significantly contributing to the semantic web. In this paper, a framework for information retrieval from XML documents using Self Adaptive Migration model Genetic Algorithms(SAGAXsearch) is proposed. Experiments on real data performed to evaluate the precision and the query execution time indicate that the framework is accurate and efficient compared to the existing techniques.

Selective dissemination of XML documents using GAs and SVM

XML has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Self Adaptive Migration Model Genetic Algorithm (SAMGA)[5] and multi class Support Vector Machine (SVM) are used to learn a user model. Based on the feedback from the users the system automatically adapts to the user’s preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

Dynamic Subspace Clustering for Very Large High-Dimensional Databases

Emerging high-dimensional data mining applications needs to find interesting clusters embeded in arbitrarily aligned subspaces of lower dimensionality. It is difficult to cluster high-dimensional data objects, when they are sparse and skewed. Updations are quite common in dynamic databases and they are usually processed in batch mode. In very large dynamic databases, it is necessary to perform incremental cluster analysis only to the updations. We present a incremental clustering algorithm for subspace clustering in very high dimensions, which handles both insertion and deletions of datapoints to the backend databases.