Journals Information
World Journal of Computer Application and Technology(CEASE PUBLICATION) Vol. 5(2), pp. 24 - 29
DOI: 10.13189/wjcat.2017.050202
Reprint (PDF) (380Kb)
Outlier Detection and Removal Algorithm in K-Means and Hierarchical Clustering
Anwesha Barai (Deb) , Lopamudra Dey *
Department of Computer Science & Engineering, Heritage Institute of Technology, Kolkata, India
ABSTRACT
An outlier in a pattern is dissimilar with rest of the pattern in a dataset. Outlier detection is an important issue in data mining. It has been used to detect and remove anomalous objects from data. Outliers occur due to mechanical faults, changes in system behavior, fraudulent behavior, and human errors. This paper describes the methodology or detecting and removing outlier in K-Means and Hierarchical clustering. First apply clustering algorithm K-means and Hierarchical clustering on a data set then find outliers from the each resulting clustering. In K-Means clustering outliers are found by distance based approach and cluster based approach. In case of hierarchical clustering, by using dendrogram outliers are found. The goal of the project is to detect the outlier and remove the outliers to make the clustering more reliable.
KEYWORDS
Outlier, Clustering, K-means, Hierarchical, Accuracy, Cophenetic Correlation Coefficient
Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Anwesha Barai (Deb) , Lopamudra Dey , "Outlier Detection and Removal Algorithm in K-Means and Hierarchical Clustering," World Journal of Computer Application and Technology(CEASE PUBLICATION), Vol. 5, No. 2, pp. 24 - 29, 2017. DOI: 10.13189/wjcat.2017.050202.
(b). APA Format:
Anwesha Barai (Deb) , Lopamudra Dey (2017). Outlier Detection and Removal Algorithm in K-Means and Hierarchical Clustering. World Journal of Computer Application and Technology(CEASE PUBLICATION), 5(2), 24 - 29. DOI: 10.13189/wjcat.2017.050202.