Results 21 to 30 of about 559,125 (302)

CLUSTERING OF EARTHQUAKE RISK IN INDONESIA USING K-MEDOIDS AND K-MEANS ALGORITHMS

open access: yesMedia Statistika, 2020
Earthquake is the shaking of the earth's surface due to the shift in the earth's plates. This disaster often happens in Indonesia due to the location of the country on the three largest plates in the world and nine small others which meet at an area to ...
Isna Hidayatur Rifa   +2 more
doaj   +1 more source

K-Means Algorithm Implementation for Project Health Clustering

open access: yesJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2023
Indonesia has several companies that are involved in the telecommunications sector. Various projects run in parallel to support the success of telecommunications companies. The potential of a project can increase company revenue and productivity.
Ajeng Arifa Chantika Rindu   +2 more
doaj   +1 more source

A parametric k-means algorithm [PDF]

open access: yesComputational Statistics, 2007
The k points that optimally represent a distribution (usually in terms of a squared error loss) are called the k principal points. This paper presents a computationally intensive method that automatically determines the principal points of a parametric distribution.
openaire   +4 more sources

Relational Algorithms for k-means Clustering

open access: yesCoRR, 2020
This paper gives a k-means approximation algorithm that is efficient in the relational algorithms model. This is an algorithm that operates directly on a relational database without performing a join to convert it to a matrix whose rows represent the data points.
Moseley, Benjamin   +3 more
openaire   +4 more sources

A Modified K-means Algorithms - Bi-Level K-Means Algorithm [PDF]

open access: yesAdvances in Intelligent Systems Research, 2014
In this paper, a modified K-means algorithm is proposed to categorize a set of data into smaller clusters. K- means algorithm is a simple and easy clustering method which can efficiently separate a huge number of continuous numerical data with high-dimensions. Moreover, the data in each cluster are similar to one another.
Chia -Yi Chuang   +4 more
openaire   +1 more source

K-Means Algorithm Analysis for Election Cluster Prediction

open access: yesJOIV: International Journal on Informatics Visualization, 2023
The general election is a democratic process that is carried out in every country whose system of government is presidential, including Indonesia, which conducts it every five years.
Sri Ngudi Wahyuni   +2 more
doaj   +1 more source

K-means** - a fast and efficient K-means algorithms

open access: yesInternational Journal of Intelligent Information and Database Systems, 2018
K-means often converges to a local optimum. In improved versions of K-means, k-means++ is well-known for achieving a rather optimum solution with its cluster initialisation strategy and high computational efficiency. Incremental K-means is recognised for its converging to the empirically global optimum but having a high complexity due to its stepping ...
Cuong Duc Nguyen, Trong Hai Duong
openaire   +1 more source

Optimization algorithm of K-means fingerprint location

open access: yesDianzi Jishu Yingyong, 2018
K-means fingerprint localization can reduce the complexity of localization, and improving the real-time of location has become a hot-spot of current localization algorithm.
Yu Chengbo, Li Caihong, Zeng Liang
doaj   +1 more source

Optimal location of logistics distribution centres with swarm intelligent clustering algorithms.

open access: yesPLoS ONE, 2022
A clustering algorithm is a solution for grouping a set of objects and for distribution centre location problems. But the common K-means clustering algorithm may give local optimal solutions. Swarm intelligent algorithms simulate the social behaviours of
Tsung-Xian Lin   +2 more
doaj   +1 more source

Adapting $k$-means algorithms for outliers

open access: yesCoRR, 2020
This paper shows how to adapt several simple and classical sampling-based algorithms for the $k$-means problem to the setting with outliers. Recently, Bhaskara et al. (NeurIPS 2019) showed how to adapt the classical $k$-means++ algorithm to the setting with outliers.
Christoph Grunau, Václav Rozhon
openaire   +3 more sources

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