@article {10.3844/jcssp.2011.1639.1645,
article_type = {journal},
title = {An Integrated Framework for Mixed Data Clustering Using Self Organizing Map},
author = {Devaraj, Hari Prasad and Punithavalli, M.},
volume = {7},
number = {11},
year = {2011},
month = {Aug},
pages = {1639-1645},
doi = {10.3844/jcssp.2011.1639.1645},
url = {https://thescipub.com/abstract/jcssp.2011.1639.1645},
abstract = {Problem statement: Clustering plays an important role in data mining of large data and
helps in analysis. This develops a vast importance in research field for providing better clustering
technique. There are several techniques exists for clustering the similar kind of data. But only very
few techniques exists for clustering mixed data items. This leads to the requirement of better
clustering technique for classification of mixed data. The cluster must be such that the similarity of
items within the clusters is increased and the similarity of items from different clusters must be
reduced. The existing techniques possess several advantages and at the same time various
disadvantages also exists. Approach: To overcome those drawbacks, Self-Organizing Map (SOM)
and Extended Attribute-Oriented Induction (EAOI) for clustering mixed data type data can be used.
This will take more time for clustering. A modified SOM was proposed based on batch learning.
Results: The experimentation for the proposed technique was carried with the help of UCI Adult
Data Set. The number of clusters resulted for the proposed technique is lesser when compared to the
usage of SOM. Also the outliers were not obtained by using the proposed technique. Conclusion:
The experimental suggests that the proposed technique can be used to cluster the mixed data items
with better accuracy of classification.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}