Respected Sir/Madam,
I want to know about:
Data MIning Classification Techniquesn is better then Clustering and why?
I think Classification and clustering are two different issues. "Classification is a data mining function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for each case in the data." and "Clustering is a process of partitioning a set of data (or objects) into a set of meaningful sub-classes, called clusters. • Help users understand the natural grouping or structure in a data set." You can follow the below mentioned link.
www.coli.uni-saarland.de/~crocker/Teaching/.../lecture13_4up.pdf
http://www.taborcommunications.com/dsstar/00/0704/101861.html
The main difference between classification and clustering techniques is classification is a supervised learning technique and clustering is unsupervised learning techniques. In supervised learning, the output values are provided which are used for learning the model and get the desired outputs whereas in unsupervised learning no output data are provided, instead the data is clustered into different group.
This is the main reason that the performance of model build using classification techniques achieve better result as compare to clustering technique.
Some commonly used classification techniques are:
Logistic regression, Decision tree, random forest, SVM, LSSVM, ELM etc..
Some commonly used clustering techniques are:
K-means, Fuzzy C-Means etc.
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