tree based data mining


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A tree-based approach for frequent pattern mining from uncertain data
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Many frequent pattern mining algorithms find patterns from traditional transaction databases,
in which the content of each transaction—namely, items—is definitely known and precise.
However, there are many real-life situations in which the content of transactions is 

Data mining and tree-based optimization
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Abstract Consider a large collection of objects, each of which has a large number of
attributes of several different sorts. We assume that there are data attributes representing
data, attributes which are to be statistically estimated or predicted from these, and 

 Distinguishing the Forest from the TREES: A Comparison of Tree-Based Data MiningMethods
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R Derrig, L Francis ,CAS Winter Forum, www. casact. org, 2008  data-mines.com
Abstract: In recent years a number of data mining approaches for modeling data containing
nonlinear and other complex dependencies have appeared in the literature. One of the key
data mining techniques is decision trees, also referred to as classification and regression 

A new fuzzy decision tree classification method for mining high-speed data streams based on binary search trees
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Z Li, T Wang, R Wang, Y Yan, H Chen ,Frontiers in Algorithmics, 2007 ,Springer
Decision tree construction is a well-studied problem in data mining. Recently, there has
been much interest in mining data streams. Domingos and Hulten have presented a one-
pass algorithm for decision tree constructions. Their system using Hoeffding inequality to 

 An efficient tree-based fuzzy data mining approach
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In the past, many algorithms were proposed for mining association rules, most of which were
based on items with binary values. In this paper, a novel tree structure called the
compressed fuzzy frequent pattern tree (CFFP tree) is designed to store the related 

 The Base Strategy for ID3 Algorithm of Data Mining Using Havrda and Charvat Entropy Basedon Decision Tree
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Abstract—Data mining is used to extract the required data from large databases [1]. The data
mining algorithm is the mechanism that creates mining models [2]. To create a model, an
algorithm first learns the rules from a set of data then looks for specific required patterns 

Prediction of the performance and effectiveness of medical equipments with decision-treearithmetic based on data mining technology
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W Ping-yang, L Yi-qun, L Mu-yan ,ZHONGGUO ZUZHI GONGCHENG , 2008 ,crter.org
Abstract: This study was aimed to study one kind of the prediction method of the
performance and effectiveness of medical equipment with decision-tree arithmetic based on
data mining technology, and predict the performance and effectiveness of medical 

 Data Mining of Modulation Types Using Cyclostationarity-Based Decision Tree
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H Xiao, C Chen, W Su, J Kosinskiand, YQ Shi
Abstract-We describe data mining of modulation types of radio frequency signals in civilian
and military communications. The cyclostationary pattern and five features derived from the
spectrum of the received communication signals are examined to formulate a decision 

 Data Mining and Tree-based Optimization
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Abstract Consider a large collection of objects, each of which has a large number of
attributes of several different sorts. We assume that there are data attributes representing
data, attributes which are to be statistically estimated from these, and attributes which can 

 Software plagiarism detection using abstract syntax tree and graph-based data mining
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HYS Hsiao ,2005 ,dc.library.okstate.edu
Since the computer was invented, software plagiarism has always been a serious problem.
People can copy other’s painstaking efforts, and pretend it is written by them or use it
arbitrarily. Plagiarism is even easier to commit in the age of the Internet. At the same time, 

 A Study on Fraud Detection Based on Data Mining Using Decision Tree
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AN Pathak, M Sehgal, D Christopher ,International Journal of Computer Science
Abstract Fraud is a million dollar business and it is increasing every year. The US identity
fraud incidence rate increased in 2008 returning to levels unseen since 2003. Almost 10
million Americans learned they were victims of identity (ID) fraud in 2008 which is up from 

Speed up gradual rule mining from stream data! A B-Tree and OWA-based approach
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Abstract Gradual rules allow users to be provided with rules describing the ordering
correlations among attributes. Such a rule is for instance given by the higher the salary and
the lower the number of cars, the higher the number of tourist travels. Previously 


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