Data mining techniques arun k pujari university press ebook

 

    Data Mining: Practical Machine Learning Tools and Techniques Solution Manual Jiawei Han and Micheline Kamber The University of Illinois at. This Book Addresses All The Major And Latest Techniques Of Data Mining And Data Arun K. Pujari. Universities Press, - Data mining - pages. Title, Data Mining Techniques. Author, Arun K. Pujari. Edition, 3, reprint. Publisher, Universities Press (India) Private Limited, ISBN,

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    Data Mining Techniques Arun K Pujari University Press Ebook

    Editorial Reviews. About the Author. Arun K Pujari is Professor of Computer Science at the Professor Pujari is at present the vice-chancellor of Sambalpur University. Product details. File Size: KB; Print Length: pages; Publisher: Universities Press (India) Pvt. Ltd. (October 15, ); Publication Date: 4, Read "Data Mining Techniques" by Arun saicumspecsacont.cf available from Rakuten Kobo. Sign up today and get $5 off your first purchase. Data Mining Techniques. Data Mining Techniques - Arun K. Pujari - Ebook download as PDF File .pdf), Text File .txt) or read book online. Arun K Pujari.

    Ncut-term weighting is recently proposed for clustering of short-texts using non-negative matrix factorization. Non-negative factorization can be employed for such term weighting when the similarity measure is the inner product of term-document matrix. We propose a new weighting scheme and devise a new clustering algorithm using Hadamard product of similarity matrices. We demonstrate that our technique yields much better clustering in comparison to ncut weighting scheme. We use three measures for evaluating clustering qualities, namely purity, normalized mutual information and adjusted Rand index. We use standard benchmark datasets and also compare the performance of our algorithm with well-known document clustering technique of Ng-Jordan-Weiss. Experimental results suggest that the weighting process by Hadamard product gives better clustering of document of short-texts.

    The feedback ratings by the users are used for providing service reputation score. Malicious and subjective user feedback often leads to bias that affects the reputation measurement of web services. In their research work, they proposed a novel system for the same. The system performed better by using Bloom filtering and proposed malicious feedback rating prevention scheme. Extensive experiments were conducted by using 1.

    Data Mining Techniques

    The experimental results showed that success ratio of the web service recommendations may be enhanced and the system might reduce the deviation of reputation measurement. In [11], the researchers proposed a novel intelligent system which would be able to detect the road accidents automatically, notify them by using vehicular networks and estimate the severity of the accident based on data mining tools and knowledge interference.

    Various variables such as the vehicle speed, the type of vehicles involved, the impact speed, and the status of the airbag, etc. Three classification algorithms were used such as Decision Trees, Support Vector Machines, and Bayesian networks and were compared for best results.

    It was found that Bayesian model for classification is the best-suited model. It can also be used for purchasing transactions under the context of mobile commerce.

    In [8], the researchers proposed a technique for the prediction of what else the customer likely to buy based on partial information about the contents of a shopping cart.

    The data structure used in this context was itemset trees ITtrees , they obtained all the rules whose antecedents contain at least one item that is missing from the shopping cart in a computationally efficient manner. The classical Bayesian decision theory and a new algorithm based on Dempster-Shafer DS theory of evidence combination were combined for finding out rules based uncertainty processing technique.

    The proposed algorithm enhanced the performance. As the input, the algorithm takes an incoming item set and returns a graph based on association rules entailed by the incoming item set. The proposed algorithm used depth-first search technique and also updated the rule graph. Association, classification, clustering, prediction, sequential pattern mining, etc.

    The input for the classification is the training set. Classification assigns class labels to unlabelled records based on a model that acquires knowledge from the training datasets.

    Such classification is known as supervised learning as the class labels are known. There are several classification models.

    Some of the common classification models are decision trees, neural networks, genetic algorithms, support vector machines, Bayesian classifiers. The application includes credit risk analysis, fraud detection, banking and medical application, etc. Clustering algorithms may be used for organizing data, categorize data for model construction and data compression, outlier detection, etc. Many clustering algorithms were developed and are categorized as partitioning methods, hierarchical methods, density based and grid based methods.

    The datasets may be numerical or categorical. The main objective is to discover all the rules that have support and confidence greater than or equal to minimum support or confidence in a database. Support means that how often X and Y occurs together as a percentage of total transactions.

    Confidence means that how much a particular item is dependent on another. There is no significance for the patterns with low confidence and support. The users can extract useful and interesting information from the patterns with intermediate values of confidence and support. The association rule mining algorithms include Apriori, AprioriTid, Apriori hybrid and Tertius algorithms [13]. It involves developing mathematical structures with ability to learn [2].

    The Neural networks have the ability to extract meaningful and useful patterns and trends from the complex data. It is applicable to real world problems especially in case of industry. As the neural networks are good at identifying patterns or trends, they may be applicable for prediction or forecasting needs.

    Data Mining – Arun K. Pujari

    The system is composed of highly interconnected processing elements neurons working together to solve a specific problem. Artificial neural network ANN learns by example [15]. ANN is configured for specific application as classification, pattern recognition etc. It may also be used for three- dimensional object recognition, hand-written word recognition, face recognition, etc. Neural networks have the drawback of not explaining the derived results. Another problem is that it suffers from long learning times.

    As the data grows, the situation becomes worse for that problem.

    The main concept is to non-linearly map the data set into a high dimensional feature space and use a linear discriminator for classification of data.

    It is basically used for regression, classification and decision tree construction. SVMs select the plane which maximizes the margin separating the two classes. The margin is defined as the distance between the separating hyperplane to the nearest point of A, plus the distance from the hyperplane to the nearest point in B, where A and B are two linearly separable sets.

    SVM has been used in many applications including face detection, handwritten character and digits recognition, speech recognition, image and information retrieval [12]. A population of the individual with possible solution to a problem is created initially at random.

    Then the crossover is done by combining pairs of individuals to produce offspring of next generation. A mutation process is used to modify the genetic structure of some members of new generation randomly. The algorithm searches for a solution in the successive generation.

    When an optimum solution is found or some fixed time is elapsed, the process comes to an end. Genetic algorithms are widely used in problems where optimization is required. Alak Kr. Buragohain, Vice-Chancellor, Dibrugarh University for his inspiring words. The author also acknowledged Prof. Christos N. It deals in detail with the algorithms for discovering association rules for clustering and building decision trees, and techniques such as neural networks, genetic algorithms, rough set theory and support vector machine used in data mining.

    The book also discusses the mining of web, spatial, temporal and text data. In the third edition, the chapter on data warehousing concepts was thoroughly revised to include multidimensional data modeling and cube computation.

    The discussion on genetic algorithms was also expanded as a separate chapter. In the fourth edition, a chapter on ROC curve for visualizing the performance of a binary classifier and the method for computing AUC and its uses has been included.

    Students of computer science, mathematical science and management will find this introductory textbook beneficial for a first course on the subject; the exposition of concepts with supporting illustrative examples and exercises makes it suitable for self-study as well. Seller Inventory We demonstrate that our technique yields much better clustering in comparison to ncut weighting scheme.

    We use three measures for evaluating clustering qualities, namely purity, normalized mutual information and adjusted Rand index. We use standard benchmark datasets and also compare the performance of our algorithm with well-known document clustering technique of Ng-Jordan-Weiss. Experimental results suggest that the weighting process by Hadamard product gives better clustering of document of short-texts.

    Data Mining Techniques by A.K. Pujari

    Preview Unable to display preview. Download preview PDF. References 1. Adamic, L. Banerjee, S. Buckley, C.