Sandeep Kumar Satapathy

Particle swarm optimization based fuzzy frequent pattern mining from gene expression data

The FP-growth algorithm is currently one of the fastest approaches to frequent item set mining. Fuzzy logic provides a mathematical framework where the entire range of the data lies in between 0 and 1. The PSO algorithm was developed from observations of the social behavior of animals, including bird flocking and fish schooling. It is easier to implement than evolutionary algorithms because it only involves a single operator for updating solutions. In contrast, evolutionary algorithms require a particular representation and specific methods for cross-over, mutation, and selection. Furthermore, PSO has been found to be very effective in a wide variety of applications, being able to produce good solutions at a very low computational cost. In this paper, we have considered the fuzzified dataset and have implemented various frequent pattern mining techniques. Out of the various frequent pattern mining techniques it was found that Frequent Pattern (FP) growth method yields us better results on a fuzzy dataset. Here, the frequent patterns obtained are considered as the set of initial population. For the selection criteria, we had considered the mean squared residue score rather using the threshold value. It was observed that out of the four fuzzy based frequent mining techniques, the PSO based fuzzy FP growth technique finds the best individual frequent patterns. Also, the run time of the algorithm and the number of frequent patterns generated is far better than the rest of the techniques used.

Improved search technique using wildcards or truncation

Search engine technology plays an important role in web information retrieval. However, with Internet information explosion, traditional searching techniques cannot provide satisfactory result due to problems such as huge number of result Web pages, unintuitive ranking etc. Therefore, the reorganization and post-processing of Web search results have been extensively studied to help user effectively obtain useful information. This paper has basically three parts. First part is the review study on how the keyword is expanded through truncation or wildcards (which is a little known feature but one of the most powerful one) by using various symbols like * or! .The primary goal in designing this is to restrict ourselves by just mentioning the keyword using the truncation or wildcard symbols rather than expanding the keyword into sentential form. Second part consists of the review on subdivision based on wildcards. It is based on the observation that documents are often found to contain terms with high information content which summarize their subject matter. The third part consists of a proposed algorithm based on the above two. The main goal of this paper is to develop a very efficient search technique by which the information retrieval will be very fast, reducing the amount of extra labor needed on expanding the query.

Fuzzy pattern tree approach for mining frequent patterns from gene expression data

Frequent pattern mining has been a focused theme in data mining research for over a decade. A lot of literature has been dedicated to this research and huge amount of work has been made, ranging from efficient and scalable algorithms for frequent item set mining in transaction databases to numerous research frontiers. Frequent pattern mining (FPM) has been applied successfully in business and scientific data for discovering interesting association patterns, and is becoming a promising strategy in microarray gene expression analysis. As we know, Fuzzy logic provides a mathematical framework that is compatible with poorly quantitative yet qualitatively significant data. In this paper, we have fuzzified our original dataset and have applied various frequent pattern mining techniques to discover meaningful frequent patterns. Also, we have drawn a clear comparison of the frequent pattern mining techniques in the original and the fuzzified data in terms of parameters like runtime of the algorithm and the number of frequent patterns generated. As a result, it was found that the fuzzified set is capable of discovering a large number of frequent patterns and have a better running time capability.

Search Technique Using Wildcards or Truncation: A Tolerance Rough Set Clustering Approach

Search engine technology plays an important role in web information retrieval. However, with Internet information explosion, traditional searching techniques cannot provide satisfactory result due to problems such as huge number of result Web pages, unintuitive ranking etc. Therefore, the reorganization and post-processing of Web search results have been extensively studied to help user effectively obtain useful information. This paper has basically three parts. First part is the review study on how the keyword is expanded through truncation or wildcards (which is a little known feature but one of the most powerful one) by using various symbols like * or! The primary goal in designing this is to restrict ourselves by just mentioning the keyword using the truncation or wildcard symbols rather than expanding the keyword into sentential form. The second part of this paper gives a brief idea about the tolerance rough set approach to clustering the search results. In tolerance rough set approach we use a tolerance factor considering which we cluster the information rich search result and discard the rest. But it may so happen that the discarded results do have some information which may not be up to the tolerance level; still they do contain some information regarding the query. The third part depicts a proposed algorithm based on the above two and thus solving the above mentioned problem that usually arise in the tolerance rough set approach . The main goal of this paper is to develop a search technique through which the information retrieval will be very fast, reducing the amount of extra labor needed on expanding the query.

An Empirical Analysis of Training Algorithms of Neural Networks: A Case Study of EEG Signal Classification Using Java Framework

With the pace of modern lifestyle, about 40–50 million people in the world suffer from epilepsy—a disease with neurological disorder. Electroencephalography (EEG) is the process of recording brain signals that generate due to a small amount of electric discharge in brain. This may occur due to the information flow among several neurons. Therefore, in every minute, analysis of EEG signal can solve much neurological disorders like epilepsy. In this paper, a systematic procedure for analysis and classification of EEG signal is discussed for identification of epilepsy in a human brain. The analysis of EEG signal is made through a series of steps from feature extraction to classification. Feature extraction from EEG signal is done through discrete wavelet transform (DWT), and the classification task is carried out by MLPNN based on supervised training algorithms such as backpropagation, resilient propagation (RPROP), and Manhattan update rule. Experimental study in a Java platform confirms that RPROP trained MLPNN to classify EEG signal is promising as compared to back-propagation or Manhattan update rule trained MLPNN.

Genetic Algorithm Based Fuzzy Frequent Pattern Mining from Gene Expression Data

Efficient algorithms have been developed for mining frequent patterns in traditional data where the content of each transaction is definitely known. It is a core technique used in many mining tasks like sequential pattern mining, correlative mining etc. As we know, fuzzy logic provides a mathematical framework that is compatible with poorly quantitative yet qualitatively significant data. Genetic algorithm (GA) is one of the optimization algorithms, which is invented to mimic some of the processes observed in natural evolution. It is a stochastic search technique based on the mechanism of natural selection and natural genetics. That is a general one, capable of being applied to an extremely wide range of problems. In this paper, we have fuzzified our original dataset and have applied various frequent pattern mining techniques on it. Then the result of a particular frequent pattern mining technique that is frequent pattern (FP) growth is taken into consideration in which we apply the concept of GA. Here, the frequent patterns observed are considered as the set of initial population. For the selection criteria, we consider the mean squared residue score rather using the threshold value. It was observed that out of the three fuzzy based frequent mining techniques and the GA based fuzzy FP growth technique the later finds the best individual frequent patterns. Also, the run time of the algorithm and the number of frequent patterns generated is far better than the rest of the techniques used. To extend our findings we have also compared the results obtained by the GA based fuzzy FP growth with an usual approach on a normalized dataset and then applied the concept of FP growth to find the frequent patterns followed by GA. Then by analyzing the result we found that GA based fuzzy FP growth stills yields the best individual frequent patterns.

A comparative study on homology modeling of P-glycoprotein (P- gp ) structure using computational approach

Proteins are composition of amino acid. These amino acids mainly form thousands of different proteins. P-glycoprotein (P-gp), one of the protein which is one of the plasma membrane and xenobiotic transport protein. It transports a variety of drug substrates. P-gp, which is encoded as ABCB1 and also shows a mechanism to protect the body from harmful substances. It also acts as a drug export pump. In this paper, P-gp has been taken as the target sequence and the protein is processed under homology modeling. To know the proper molecular model of the protein, the target sequence is matched with the protein structure database with the help of protein data bank to find out the template. The target sequence has been matched with protein structure database with the help of bioinformatics tool and also with some of the other programming techniques. The maximum identity accession along with the P-gp structure is identified. The homologous protein structure is now analyzed and compared with the existing P-gp structure.

PATTERN BASED SUBSPACE CLUSTERING: A REVIEW

The task of biclustering or subspace clustering is a data mining technique that allows simultaneous clustering of rows and columns of a matrix. Though the definition of similarity varies from one biclustering model to another, in most of these models the concept of similarity is often based on such metrics as Manhattan distance, Euclidean distance or other Lp distances. In other words, similar objects must have close values in at least a set of dimensions. Pattern-based clustering is important in many applications, such as DNA micro-array data analysis, automatic recommendation systems and target marketing systems. However, pattern-based clustering in large databases is challenging. On the one hand, there can be a huge number of clusters and many of them can be redundant and thus makes the pattern-based clustering ineffective. On the other hand, the previous proposed methods may not be efficient or scalable in mining large databases. The objective of this paper is to perform a comparative study of all subspace clustering algorithms in terms of efficiency, accuracy and time complexity.