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,

Data Mining Techniques Arun K Pujari University Press Ebook

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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 available from Rakuten Kobo. Sign up today and get $5 off your first download. 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.

It is also extensively used for language processing.

NLTK played a major role as a teaching tool, study tool, prototyping and can be used as a platform for high-quality research. Students Mood recognition [3] was proposed by Christos N. Moridis et. Exponential logic and formulas were used in this regards. Appropriate feedbacks are recorded based on current status of moods of the students. A novel weakly supervised cyber criminal network mining method [18] was proposed by Raymond Y.

Lau et. The technique was based on relationships both explicit and implicit among the cyber criminals. The messages posted by these criminals on the social media were the basis of this method. The algorithm used in this context was context-sensitive Gibbs sampling algorithm.

The algorithm mined both transactional and collaborative semantics to find the relationship among such criminals.

The model used was a probabilistic generative model for extracting multi-word expressions.

Two types of cyber criminal relationships were established in unlabeled messages. The approach used here is concept level for the implicit semantics associated with the text.

Shenghua Bao et. For such social affective text mining, a joint emotion-topic model was proposed by introducing an additional layer for such kind of emotion modeling into Latent Dirichlet Allocation LDA.

Associate emotions with specific emotional context were used instead of a single term. The authors developed an approximate inference model by using Gibbs Sampling Algorithm. The model categorized text based on different emotions such as touch, surprise, and empathy, etc.

Luigi Lancieri et. Two different kinds of categorization algorithms were used.

Data Mining Techniques

Hierarchical agglomerative clustering HAC was used for hard clustering. Li-Der Chou et. Families with CDD, university, hospital and foundation came hand to hand to share significant information based on online social network related to childcare of such children. The users can access the application with the help of PDA, personal computer or mobile devices by installing the application on such devices.

Data Mining – Arun K. Pujari

In [17], the authors used distributional features of text categorization that took into account the compactness and the position of the first appearance of the word. The authors in their research work explored other types of values which express distribution of word in a document. The distributional features are used by a tf idf style equation and features of different categories are combined using ensemble learning techniques.

The authors proved experimentally that distributional features are useful for text categorisation. The categorisation performance improves significantly by using these features with little additional cost in contrast to traditional methods. The distribution features performances are enhanced the case of long documents and when the writing style is casual.

Data Mining Techniques - Arun K. Pujari

In [15], the authors designed web service recommendation systems. While designing web service recommendation systems, the focused research problem was to avoid recommending unfair or poor services to the users.

The system should help users to choose right service from the huge number of available web services. The widely recommended metric in this regards is the reputation of web services. 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. 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 downloading transactions under the context of mobile commerce. In [8], the researchers proposed a technique for the prediction of what else the customer likely to download 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.

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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.

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].

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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. Data mining. Association rules. Clustering techniques. Decision trees. Rough set theory. Genetic algorithm. Other techniques. Performance evaluation - ROC curve.

Web mining. Temporal and spatial data mining. This book addresses all the major and latest techniques of data mining.Database technology had evolved from primitive file processing to the development of data mining tools and applications.

Extensive experiments were conducted by using 1. Various variables such as the vehicle speed, the type of vehicles involved, the impact speed, and the status of the airbag, etc.

Buragohain, Vice-Chancellor, Dibrugarh University for his inspiring words. Malicious and subjective user feedback often leads to bias that affects the reputation measurement of web services. Spatial data description, classification, association, clustering, trend, and outlier analysis are the main components for spatial data mining. The categorisation performance improves significantly by using these features with little additional cost in contrast to traditional methods.

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