Classification is a classical problem in machine learning
and data mining. Given a set of training data tuples, each
having a class label and being represented by a feature vector,
the task is to algorithmically build a model that predicts
the class label of an unseen test tuple based on the tuple s
feature vector. One of the most popular classification models
is the decision tree model. Decisions trees are popular because
they are practical and easy to understand. Rules can also be
extracted from decision trees easily. Many algorithms ....etc

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Abstract
Efficiency and scalability are fundamental issues concerning data mining in large databases. Although classification has been studied extensively, few of the known methods take serious consideration of efficient induction in large databases and the analysis of data at multiple abstraction levels. The paper addresses the efficiency and scalability issues by proposing a data classification method which integrates attribute oriented induction, relevance analysis, and the induction of decision trees. Such an integration leads to efficient ....etc

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i need matlab code for classifying mammographic images using svm classifier ....etc

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I need some project details for the topicPrivacy preserving decision tree for uncertain data in dotnet pls assist me ....etc

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Abstract Privacy preservation is important for machine learning and data mining, but measures designed to protect private information often result in a trade-off: reduced utility of the training samples. This paper introduces a privacy preserving approach that can be applied to decision tree learning, without concomitant loss of accuracy. It describes an approach to the preservation of the privacy of collected data samples in cases where information from the sample database has been partially lost. This approach converts the original sample da ....etc

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to get information about the topic privacy preserving decision tree learning using unrealized data sets related topic refer the page link bellow

http://seminarsprojects.net/Thread-privacy-preserving-decision-tree-learning-using-unrealized-data-sets?pid=63305&mode=threaded ....etc

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to get information about the topic privacy preserving decision tree learning using unrealized data sets related topic refer the page link bellow

http://seminarsprojects.net/Thread-privacy-preserving-decision-tree-learning-using-unrealized-data-sets?pid=63305&mode=threaded ....etc

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Abstract

Traditional decision tree classifiers work with data whose values are known and precise. We extend such classifiers to handle data with uncertain information. Value uncertainty arises in many applications during the data collection process. Example sources of uncertainty include measurement/quantization errors, data staleness, and multiple repeated measurements. With uncertainty, the value of a data item is often represented not by one single value, but by multiple values forming a probability distribution. Rather than abstracting unc ....etc

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to get information about the topic privacy preserving decision tree learning using unrealized data sets 2012 full report refer the page link bellow

http://seminarsprojects.net/Thread-privacy-preserving-decision-tree-learning-using-unrealized-data-sets?pid=63305&mode=threaded ....etc

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why is classifier used in tree based mining for discovering patterns of human interactions in meetings? ....etc

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