Decision Trees

Basics of Decision Trees

A flow-chart-like hierarchical tree structure
Often restricted to a binary structure
Root: represents the entire dataset
A node without children is called a leaf node. Otherwise is called an internal node.
Internal nodes: denote a test on an attribute
Branch (split): represents an outcome of the test
Univariate split (based on a single attribute)
Multivariate split
Leaf nodes: represent class labels or class distribution

Most decision tree generation consists ....etc

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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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what are the distributed based algorithms used in the decision ....etc

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Data Warehouses, Decision Support and Data Mining

A Data Warehouse is a subject-oriented,integrated,time-variant,non-volatile collection of data used in support of management decision making processes.

Subject-Oriented:-
The data warehouse is organized around the key subjects (or high-level entities) of the enterprise.

Major subjects include
a) Customers
b) Patients
c) Students
d) Products
e) Etc.

Data support :-

Stored collection of diverse data
A solution to data integration problem
Single repository of information
Sub ....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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I am doing the project on decision tree for uncertain data.can you please tell me the future enhancements to this paper. ....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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i want source code for decision tree for uncertain data
please send source code for decision tree for uncertain data. ....etc

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In recent years, a number of indirect data collection methodologies have led to the proliferation of uncertain data. Such databases are much more complex because of the additional challenges of representing the probabilistic information. In this paper, we provide a survey of uncertain data mining and management applications. We will explore the various models utilized for uncertain data representation. In the field of uncertain data management, we will examine traditional database management methods such as join processing, query processing, se ....etc

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