Data Classification using Data Mining Techniques

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Data classification using data mining techniques is used for classify the data. Classification is performed on the input data and returns a classifiers tree as its' Output. It is a two step process-in first step; a classifier is built describing a predetermined set of data classes or concepts

The  model is constructed by analyzing data mining tuples described by attributes. In second step the classifier is used  for classification.   The predictive accuracy of the classifier is estimated. It has been tested on different samples and was observed that the tuples are successfully classified.


Ø  Data Mining is a process of extracting “knowledge” from large data repositories.

Ø  Data Mining is a process of automatically searching large volumes of data for patterns.

Ø  It makes uses of tools such as classification, association rule mining, clustering etc.


Ø  Classification is a process of determining a set of models that describe and distinguish data classes or concepts.

Ø  Then, the models are used to predict the classes of objects whose class label is not known.


The current system is maintaining manually

Ø  Manual maintenance of records involves burden and it is quite tedious task.In general existing system there is no security. 

Ø  If any record missed  it is very difficult  to retrieve the classifier tree. 


Ø  Good security is not provided by the existing system.

Ø   It is very time taking.

Ø   The complexity increases tending to a very high probability of error.


Ø  The proposed system is computerized to provide greater easiness to the users of the system.

Ø  In this system we use decision tree induction method.

Ø  The system is constructed in an object oriented trend, thinking in an abstract way considering all the involvement as objects.


Ø  Easy In Use of the System.

Ø   Providing high security to the system.

Ø   It minimizes the time complexity involved in managing transactions.

Ø   It minimizes historical database to analyze later for taking any decisions.

Ø   There is no need to spend a lot of time for checking the records.


Ø  Good security is provided by the existing system.

Ø   Security measures are taken to avoid mishandling of database.

Ø   It minimizes the man power.

Ø   It minimizes time complexity.

Ø   It is very efficient and fast.


Ø  The basic decision tree induction requires all attributes to be categorical or discredited.

Ø   The algorithm can be modified to allow for attributes that have a whole range of

Ø      continuous or discrete values.

Ø  The information gain measure is biased in that it tends to prefer attributes with many values.

Ø   The alternative measures proposed are gain ratio, Gini index, G-statistic.


Ø  Decision Tree

Ø     Binary Node

Ø     Calling Class



Ø  Processor                               : Pentium IV

Ø  Ram capacity                        : 512MB

Ø  Hard disk capacity                :40GB 


Ø  Front end                    :JAVA

Ø  Back end                     :ORACLE

Ø  Operating system      :Windows 2000 or Windows XP  

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