Data Mining - Concepts and Techniques.

International Journal of Computer and Applications

Software testing is any activity aimed at evaluating an attribute or capability of a program or system and determining that it meets its required results. Although crucial to software quality and widely deployed by programmers and testers, software testing still remains an art, due to limited understanding of the principles of software. The difficulty in software testing stems from the complexity of software: we can not completely test a program with moderate complexity. Testing is more than just debugging. The purpose of testing can be quality assurance, verification and validation, or reliability estimation. Testing can be used as a generic metric as well. Correctness testing and reliability testing are two major areas of testing. Software testing is a trade-off between budget, time and quality.

Software testing

The massive growth in the scale of data has been observed in recent years being a key factor of the Big Data scenario. Big Data can be defined as high volume, velocity and variety of data that require a new high-performance processing. Addressing big data is a challenging and time-demanding task that requires a large computational infrastructure to ensure successful data processing and analysis. The presence of data preprocessing methods for data mining in big data is reviewed in this paper. The definition, characteristics, and categorization of data preprocessing approaches in big data are introduced. The connection between big data and data preprocessing throughout all families of methods and big data technologies are also examined, including a review of the state-of-the-art. In addition, research challenges are discussed, with focus on developments on different big data framework, such as Hadoop, Spark and Flink and the encouragement in devoting substantial research efforts in some families of data preprocessing methods and applications on new big data learning paradigms.

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Big data preprocessing: methods and prospects

An advanced ACO algorithm for feature subset selection

A new model based approach for automated generation of test cases in object oriented systems has been presented. The test cases are derived by analyzing the dynamic behavior of the objects due to internal and external stimuli. The scope of the paper has been limited to the object diagrams taken from the Unified Modeling Language model of the system. Genetic Algorithm’s tree crossover has been proposed to bring out all possible test cases of a given object diagram. Illustrative case study has been presented to establish the effectiveness of our methodology coupled with mutation analysis

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Automatic Test Case Generation for UML Object diagrams using Genetic Algorithm

The rapid expansion of digital data content has led to the need for rich descriptions and efficient Retrieval Tool. To develop this, content based image Retrieval method has played an important role in the field of image retrieval. This paper aims to provide an efficient medical image data Retrieval from a huge content of medical database using one of the images content such as image shape, because, efficient content-based image Retrieval in the medical domain is still a challenging problem. The main objective of this paper is to provide an efficient tool which is used for efficient medical image retrieval from a huge content of medical image database and which is used for further medical diagnosis purposes.

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Content based Image Retrieval for Medical Images using Canny Edge Detection Algorithm

Feature selection is an indispensable preprocessing step for effective analysis of high dimensional data. It removes irrelevant features, improves the predictive accuracy and increases the comprehensibility of the model constructed by the classifiers sensitive to features. Finding an optimal feature subset for a problem in an outsized domain becomes intractable and many such feature selection problems have been shown to be NP-hard. Optimization algorithms are frequently designed for NP-hard problems to find nearly optimal solutions with a practical time complexity. This paper formulates the text feature selection problem as a combinatorial problem and proposes an Ant Colony Optimization (ACO) algorithm to find the nearly optimal solution for the same. It differs from the earlier algorithm by Aghdam et al. by including a heuristic function based on statistics and a local search. The algorithm aims at determining a solution that includes 'n' distinct features for each category. Optimization algorithms based on wrapper models show better results but the processes involved in them are time intensive. The availability of parallel architectures as a cluster of machines connected through fast Ethernet has increased the interest on parallelization of algorithms. The proposed ACO algorithm was parallelized and demonstrated with a cluster formed with a maximum of six machines. Documents from 20 newsgroup benchmark dataset were used for experimentation. Features selected by the proposed algorithm were evaluated using Naive bayes classifier and compared with the standard feature selection techniques. It was observed that the performance of the classifier had been improved with the features selected by the enhanced ACO and local search. Error of the classifier decreases over iterations and it was observed that the number of positive features increases with the number of iterations.

An enhanced ACO algorithm to select features for text categorization and its parallelization

Text Classification is enduring to be one of the most researched problems due to continuously-increasing amount of electronic documents and digital data. Naive Bayes is an effective and a simple classifier for data mining tasks, but does not show much satisfactory results in automatic text classification problems. In this paper, the performance of Naive Bayes classifier is analyzed by training the classifier with only the positive features selected by CHIR, a statistics based method as input. Feature selection is the most important preprocessing step that improves the efficiency and accuracy of text classification algorithms by removing redundant and irrelevant terms from the training corpus. Experiments were conducted for randomly selected training sets and the performance of the classifier with words as features was analyzed. The proposed method achieves higher classification accuracy compared to other native methods for the 20Newsgroup benchmark.

Naïve Bayes text classification with positive features selected by statistical method

Problem statement: Recently, there has been a huge progress in collection of varied image databases in the form of digital. Most of the users found it difficult to search and retrieve required images in large collections. In order to provide an effective and efficient search engine tool, the system has been implemented. In image retrieval system, there is no methodologies have been considered directly to retrieve the images from databases. Instead of that, various visual features that have been considered indirect to retrieve the images from databases. In this system, one of the visual features such as texture that has been considered indirectly into images to extract the feature of the image. That featured images only have been considered for the retrieval process in order to retrieve exact desired images from the databases. Approach: The aim of this study is to construct an efficient image retrieval tool namely, Content Based Medical Image Retrieval with Texture Content using Gray Level Co-occurrence Matrix (GLCM) and k-Means Clustering algorithms. This image retrieval tool is capable of retrieving images based on the texture feature of the image and it takes into account the Pre-processing, feature extraction, Classification and retrieval steps in order to construct an efficient retrieval tool. The main feature of this tool is used of GLCM of the extracting texture pattern of the image and k-means clustering algorithm for image classification in order to improve retrieval efficiency. The proposed image retrieval system consists of three stages i.e., segmentation, texture feature extraction and clustering process. In the segmentation process, preprocessing step to segment the image into blocks is carried out. A reduction in an image region to be processed is carried out in the texture feature extraction process and finally, the extracted image is clustered using the k-means algorithm. The proposed system is employed for domain specific based search engine for medical Images such as CT-Scan, MRI-Scan and X-Ray. Results: For retrieval efficiency calculation, conventional measures namely precision and recall were calculated using 1000 real time medical images (100 in each category) from the MATLAB Workspace database. For selected query images from the MATLAB-Image Processing tool Box-Workspace Database, the proposed tool was tested and the precision and recall results were presented. The result indicates that the tool gives better performance in terms of percentage for all the 1000 real time medical images from which the scalable performance of the system has been proved. Conclusion: This study proposed a model for the Content Based Medical Image Retrieval System by using texture feature in calculating the Gray Level Co Occurrence matrix (GLCM) from which various statistical measures were computed in order to increasing similarities between query image and database images for improving the retrieval performance along with the large scalability of the databases.

K. R. Chandran

Papers

Automatic Test Case Generation for UML Object diagrams using Genetic Algorithm

Content based Image Retrieval for Medical Images using Canny Edge Detection Algorithm

An enhanced ACO algorithm to select features for text categorization and its parallelization

Naïve Bayes text classification with positive features selected by statistical method

Content Based Medical Image Retrieval with Texture Content Using Gray Level Co-occurrence Matrix and K-Means Clustering Algorithms