Outlier Detection and Removal Algorithm in K-Means and Hierarchical Clustering

@inproceedings{Barai2017OutlierDA,
  title={Outlier Detection and Removal Algorithm in K-Means and Hierarchical Clustering},
  author={Anwesha Barai and Lopamudra Dey},
  year={2017}
}
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… 
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References

SHOWING 1-10 OF 14 REFERENCES
Outlier Detection over Data Set Using Cluster-Based and Distance-Based Approach
TLDR
Proposed method for outlier detection takes less computational cost and performs better than the distance based method, and efficiently prunes of the safe cells (inliers) and save huge number of extra calculations.
A Study of Clustering Based Algorithm for Outlier Detection in Data streams
Recently many researchers have focused on mining data streams and they proposed many techniquesand algorithms for data streams. It refers to the process of extracting knowledge from nonstop fast
Improving K-Means by Outlier Removal
TLDR
An Outlier Removal Clustering (ORC) algorithm that provides outlier detection and data clustering simultaneously and has a lower error on datasets with overlapping clusters than the competing methods is presented.
Cluster Based Outlier Detection Algorithm for Healthcare Data
TLDR
Results show that the cluster-based outlier detection algorithm providing better accuracy than distance based outlier Detection algorithm for detecting and removing outlier score.
A New Procedure of Clustering Based on Multivariate Outlier Detection
Clustering is an extremely important task in a wide variety of application domains especially in management and social science research. In this paper, an iterative procedure of clustering method
Canonical PSO Based K-Means Clustering Approach for Real Datasets
TLDR
Canonical PSO based K-means clustering algorithm is proposed and some important clustering indices (intercluster, intracluster) are analyzed and the effects of those indices on real-time air pollution database, wholesale customer, wine, and vehicle datasets are evaluated.
A Review of K-mean Algorithm
TLDR
Three dissimilar modified k- mean algorithm are discussed which remove the limitation of k-mean algorithm and improve the speed and efficiency of k -mean algorithm.
A Systematic Review of Outliers Detection Techniques in Medical Data - Preliminary Study
TLDR
Outlier detection techniques can be used to detect abnormal patterns in health records contributing to better data and better knowledge in the process of decision.
Combining and comparing clustering and layout algorithms
Many clustering and layout techniques have been used for structuring and visualising complex data. This paper explores a number of combinations and variants of sampling, K-means clustering and spring
Outlier Detection using Clustering Methods: a data cleaning application
The present invention provides a heat exchange element comprising a molded product of a paper-like material made of ceramic fibers as a matrix, the interstices among the ceramic fibers being
...
1
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