Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

Outlier detection is an important data analysis task in its own right and removing the outliers
from clusters can improve the clustering accuracy. In this paper, we extend the k-means …

Ensemble analysis is a popular method used to improve the accuracy of various data mining
algorithms. Ensemble methods combine the outputs of multiple algorithms or base detectors …

We study the problem of k-means clustering in the presence of outliers. The goal is to cluster
a set of data points to minimize the variance of the points assigned to the same cluster, with …

In this paper, we present a new iterative rounding framework for many clustering problems.
Using this, we obtain an (α1+ є≤ 7.081+ є)-approximation algorithm for k-median with …

Cluster analysis and outlier detection are two continuously rising topics in data mining area,
which in fact connect to each other deeply. Cluster structure is vulnerable to outliers; …

Random swap algorithm aims at solving clustering by a sequence of prototype swaps, and
by fine-tuning their exact location by k-means. This randomized search strategy is simple to …

Abstract k-Means is a well-studied clustering problem that finds applications in many fields
related to unsupervised learning. It is known that k-means clustering is highly sensitive to the …

We present a novel algorithm for anomaly detection on very large datasets and data
streams. The method, named EXPected Similarity Estimation (expose), is kernel-based and …

Plain vanilla K-means clustering has proven to be successful in practice, yet it suffers from
outlier sensitivity and may produce highly unbalanced clusters. To mitigate both …