[HTML][HTML] An adaptive outlier removal aided k-means clustering algorithm
K-means is one of ten popular clustering algorithms. However, k-means performs poorly due
to the presence of outliers in real datasets. Besides, a different distance metric makes a …
to the presence of outliers in real datasets. Besides, a different distance metric makes a …
A local search algorithm for k-means with outliers
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 …
related to unsupervised learning. It is known that k-means clustering is highly sensitive to the …
Diversity maximization in the presence of outliers
D Amagata - Proceedings of the AAAI conference on artificial …, 2023 - ojs.aaai.org
Given a set X of n points in a metric space, the problem of diversity maximization is to extract
a set S of k points from X so that the diversity of S is maximized. This problem is essential in …
a set S of k points from X so that the diversity of S is maximized. This problem is essential in …
Fast algorithms for distributed k-clustering with outliers
In this paper, we study the $ k $-clustering problems with outliers in distributed setting. The
current best results for the distributed $ k $-center problem with outliers have quadratic local …
current best results for the distributed $ k $-center problem with outliers have quadratic local …
The identification of clusters of risk factors and their association with hospitalizations or emergency department visits in home health care
Aims To identify clusters of risk factors in home health care and determine if the clusters are
associated with hospitalizations or emergency department visits. Design A retrospective …
associated with hospitalizations or emergency department visits. Design A retrospective …
Imbalanced clustering with theoretical learning bounds
J Zhang, H Tao, C Hou - IEEE Transactions on Knowledge and …, 2023 - ieeexplore.ieee.org
Imbalanced clustering, where the number of samples varies in different clusters, has arisen
from many real data mining applications. It has gained increasing attention. Nevertheless …
from many real data mining applications. It has gained increasing attention. Nevertheless …
UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis
H Guo, M Li, X Zhang, X Gao, Q Liu - Frontiers in Neurorobotics, 2022 - frontiersin.org
Indoor location information is an indispensable parameter for modern intelligent warehouse
management and robot navigation. Indoor wireless positioning exhibits large errors due to …
management and robot navigation. Indoor wireless positioning exhibits large errors due to …
Structural iterative rounding for generalized k-median problems
This paper considers approximation algorithms for generalized k-median problems. These
problems can be informally described as k-median with a constant number of extra …
problems can be informally described as k-median with a constant number of extra …
[PDF][PDF] Identification of Leaf Disease Using Machine Learning Algorithm for Improving the Agricultural System [J]
Diagnosing plant disease is the foundation for effective and accurate plant disease
prevention in a complicated environment. Smart farming is one of the fastgrowing processes …
prevention in a complicated environment. Smart farming is one of the fastgrowing processes …
Multiple-perspective clustering of passive Wi-Fi sensing trajectory data
Information about the spatiotemporal flow of humans within an urban context has a wide
plethora of applications. Currently, although there are many different approaches to collect …
plethora of applications. Currently, although there are many different approaches to collect …