A review on machine learning and deep learning perspectives of IDS for IoT: recent updates, security issues, and challenges
Abstract Internet of Things (IoT) is widely accepted technology in both industrial as well as
academic field. The objective of IoT is to combine the physical environment with the cyber …
academic field. The objective of IoT is to combine the physical environment with the cyber …
K-means properties on six clustering benchmark datasets
P Fränti, S Sieranoja - Applied intelligence, 2018 - Springer
This paper has two contributions. First, we introduce a clustering basic benchmark. Second,
we study the performance of k-means using this benchmark. Specifically, we measure how …
we study the performance of k-means using this benchmark. Specifically, we measure how …
Explainable k-means and k-medians clustering
M Moshkovitz, S Dasgupta… - … on machine learning, 2020 - proceedings.mlr.press
Many clustering algorithms lead to cluster assignments that are hard to explain, partially
because they depend on all the features of the data in a complicated way. To improve …
because they depend on all the features of the data in a complicated way. To improve …
Better Guarantees for -Means and Euclidean -Median by Primal-Dual Algorithms
Clustering is a classic topic in optimization with k-means being one of the most fundamental
such problems. In the absence of any restrictions on the input, the best-known algorithm for k …
such problems. In the absence of any restrictions on the input, the best-known algorithm for k …
Turning Big Data Into Tiny Data: Constant-Size Coresets for -Means, PCA, and Projective Clustering
We develop and analyze a method to reduce the size of a very large set of data points in a
high-dimensional Euclidean space R^d to a small set of weighted points such that the result …
high-dimensional Euclidean space R^d to a small set of weighted points such that the result …
[图书][B] Introduction to high-dimensional statistics
C Giraud - 2021 - taylorfrancis.com
Praise for the first edition:"[This book] succeeds singularly at providing a structured
introduction to this active field of research.… it is arguably the most accessible overview yet …
introduction to this active field of research.… it is arguably the most accessible overview yet …
Socially fair k-means clustering
We show that the popular k-means clustering algorithm (Lloyd's heuristic), used for a variety
of scientific data, can result in outcomes that are unfavorable to subgroups of data (eg …
of scientific data, can result in outcomes that are unfavorable to subgroups of data (eg …
Fair Coresets and Streaming Algorithms for Fair k-means
We study fair clustering problems as proposed by Chierichetti et al.[CKLV17]. Here, points
have a sensitive attribute and all clusters in the solution are required to be balanced with …
have a sensitive attribute and all clusters in the solution are required to be balanced with …
On the cost of essentially fair clusterings
Clustering is a fundamental tool in data mining. It partitions points into groups (clusters) and
may be used to make decisions for each point based on its group. However, this process …
may be used to make decisions for each point based on its group. However, this process …
Scalable kernel k-means clustering with nystrom approximation: Relative-error bounds
Kernel k-means clustering can correctly identify and extract a far more varied collection of
cluster structures than the linear k-means clustering algorithm. However, kernel k-means …
cluster structures than the linear k-means clustering algorithm. However, kernel k-means …