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Classification organized examples to increase a model

                Classification technique is generally used in
data mining, which has a set of organized examples to increase a model that can
categorize the population documentation at large. This technique frequently
used in neural network based algorithm or decision tree algorithms. The data
classification consist Learning and Classification. In learning system, training
data considered by the classification algorithms. Here the classification test
data are used to estimate the precision of the classification rules. If the
precision is acceptable rules can be requested to the new data record. 3 9

The Classification Methods are,

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1.       Support vector machine,

2.       Neural networks,

3.       Bayesian classification,

4.       Classification based decision tree induction,

5.       Classification based Association.

 

·        
Clustering:

                Clustering
is a process of collection a set of objects into multiple groups. The groups
are called clusters, so that clusters have more similarity, but are very
dissimilar to objects in other clusters. These similar and dissimilar
activities are based on attribute values describing the objects and often involves
in distance calculations. Clustering used many application areas like biology,
security, business intelligent, and Web search. 39

The Clustering
Methods are,

1.       Partitioning Methods,

2.       Hierarchical Methods,

3.       Density-Based Methods,

4.       Grid-Based Methods,

5.       Model-Based Methods.

 

·        
Regression:

Regression technique is plan a data item to a real-valued
prediction variable. Regression is also called supervised learning technique.
This technique analyzed the dependency of some attribute values, which is
dependent on the values of the other attributes, present in same items. In this
Regression technique the intention value is already known one. For example, we
can find behavior of child is based on their family history. 38

The Regression Methods are,

1.       Simple Regression Linear Model,

2.       Logistic Regression,

3.       Polynomial Regression,

4.       Stepwise Regression,

5.       Lasso Regression, etc.

 

·        
Association:

Association techniques determine the probability of
co-occurrence of the items in a collection. The relationships between
co-occurring items are expressed as association rules. Retailers can use this type of rules to help them identify new
opportunities for cross-selling their products to the customers. Market basket
data, association analysis is also related to other application such as
bioinformatics, medical diagnosis, Web mining, and scientific data analysis.

 

·        
Time series analysis:

Time series analyses contain methods for analyzing time series
data in order to extract meaningful statistics and other characteristics of the
data. Time series forecasting is the use of a model to predict future values
based on earlier observed values. Models for time series data can have many
forms and represent different random processes. There are different types of motivation and data analysis
available for time series which are appropriate for different purposes and etc.
They are,

1.       Exploratory analysis,

2.       Curve fitting,

3.       Function approximation,

4.       Prediction and forecasting,

5.       Signal estimation, etc. 10

 

·        
Summarization:

Summarization is a key data mining concept which involves
techniques for finding a solid description of a dataset. This technique has some
approaches like,

1.       Two-Step approach to summarization,

2.       Bottom-up approach to summarization.

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