[图书][B] An introduction to outlier analysis
CC Aggarwal, CC Aggarwal - 2017 - Springer
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 …
mining and statistics literature. In most applications, the data is created by one or more …
Progress in outlier detection techniques: A survey
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 …
application areas. Researchers continue to design robust schemes to provide solutions to …
Multi-view clustering: A survey
Y Yang, H Wang - Big data mining and analytics, 2018 - ieeexplore.ieee.org
In the big data era, the data are generated from different sources or observed from different
views. These data are referred to as multi-view data. Unleashing the power of knowledge in …
views. These data are referred to as multi-view data. Unleashing the power of knowledge in …
Outlier detection using AI: a survey
MNK Sikder, FA Batarseh - AI Assurance, 2023 - Elsevier
An outlier is an event or observation that is defined as an unusual activity, intrusion, or a
suspicious data point that lies at an irregular distance from a population. The definition of an …
suspicious data point that lies at an irregular distance from a population. The definition of an …
Ensembles for unsupervised outlier detection: challenges and research questions a position paper
Ensembles for unsupervised outlier detection is an emerging topic that has been neglected
for a surprisingly long time (although there are reasons why this is more difficult than …
for a surprisingly long time (although there are reasons why this is more difficult than …
Adjusting for chance clustering comparison measures
Adjusted for chance measures are widely used to compare partitions/clusterings of the same
data set. In particular, the Adjusted Rand Index (ARI) based on pair-counting, and the …
data set. In particular, the Adjusted Rand Index (ARI) based on pair-counting, and the …
Outlier ensembles: position paper
CC Aggarwal - ACM SIGKDD Explorations Newsletter, 2013 - dl.acm.org
Ensemble analysis is a widely used meta-algorithm for many data mining problems such as
classification and clustering. Numerous ensemble-based algorithms have been proposed in …
classification and clustering. Numerous ensemble-based algorithms have been proposed in …
[图书][B] Outlier ensembles
CC Aggarwal, CC Aggarwal - 2017 - Springer
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 …
algorithms. Ensemble methods combine the outputs of multiple algorithms or base detectors …
Mixture correntropy based robust multi-view K-means clustering
Multi-view clustering has been a significant research problem in unsupervised clustering in
recent years and has important applications in computer vision, data mining and other fields …
recent years and has important applications in computer vision, data mining and other fields …
Standardized mutual information for clustering comparisons: one step further in adjustment for chance
Mutual information is a very popular measure for comparing clusterings. Previous work has
shown that it is beneficial to make an adjustment for chance to this measure, by subtracting …
shown that it is beneficial to make an adjustment for chance to this measure, by subtracting …