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 has been a topic in statistics for centuries. Over mainly the last two
decades, there has been also an increasing interest in the database and data mining …

Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …

An integrated framework for density-based cluster analysis, outlier detection, and data
visualization is introduced in this article. The main module consists of an algorithm to …

The evaluation of unsupervised outlier detection algorithms is a constant challenge in data
mining research. Little is known regarding the strengths and weaknesses of different …

As an essential task in data mining, outlier detection identifies abnormal patterns in
numerous applications, among which clustering-based outlier detection is one of the most …

Practical building operations usually deviate from the designed building operational
performance due to the wide existence of operating faults and improper control strategies …

Learning expressive low-dimensional representations of ultrahigh-dimensional data, eg,
data with thousands/millions of features, has been a major way to enable learning methods …

Ensemble analysis has recently been studied in the context of the outlier detection problem.
In this paper, we investigate the theoretical underpinnings of outlier ensemble analysis. In …

Building operations account for the largest proportion of energy use throughout the building
life cycle. The energy saving potential is considerable taking into account the existence of a …