K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data
Advances in recent techniques for scientific data collection in the era of big data allow for the
systematic accumulation of large quantities of data at various data-capturing sites. Similarly …
systematic accumulation of large quantities of data at various data-capturing sites. Similarly …
A unifying review of deep and shallow anomaly detection
Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …
the art in detection performance on complex data sets, such as large collections of images or …
A survey on machine learning techniques for cyber security in the last decade
Pervasive growth and usage of the Internet and mobile applications have expanded
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …
A comprehensive survey of loss functions in machine learning
Q Wang, Y Ma, K Zhao, Y Tian - Annals of Data Science, 2020 - Springer
As one of the important research topics in machine learning, loss function plays an important
role in the construction of machine learning algorithms and the improvement of their …
role in the construction of machine learning algorithms and the improvement of their …
Unsupervised K-means clustering algorithm
KP Sinaga, MS Yang - IEEE access, 2020 - ieeexplore.ieee.org
The k-means algorithm is generally the most known and used clustering method. There are
various extensions of k-means to be proposed in the literature. Although it is an …
various extensions of k-means to be proposed in the literature. Although it is an …
On the binding problem in artificial neural networks
Contemporary neural networks still fall short of human-level generalization, which extends
far beyond our direct experiences. In this paper, we argue that the underlying cause for this …
far beyond our direct experiences. In this paper, we argue that the underlying cause for this …
A review of local outlier factor algorithms for outlier detection in big data streams
Outlier detection is a statistical procedure that aims to find suspicious events or items that
are different from the normal form of a dataset. It has drawn considerable interest in the field …
are different from the normal form of a dataset. It has drawn considerable interest in the field …
From clustering to clustering ensemble selection: A review
Clustering, as an unsupervised learning, is aimed at discovering the natural groupings of a
set of patterns, points, or objects. In clustering algorithms, a significant problem is the …
set of patterns, points, or objects. In clustering algorithms, a significant problem is the …
A review of clustering techniques and developments
This paper presents a comprehensive study on clustering: exiting methods and
developments made at various times. Clustering is defined as an unsupervised learning …
developments made at various times. Clustering is defined as an unsupervised learning …