A comparative study of efficient initialization methods for the k-means clustering algorithm
K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately,
due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of …
due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of …
A k-means based co-clustering (kCC) algorithm for sparse, high dimensional data
The k-means algorithm is a widely used method that starts with an initial partitioning of the
data and then iteratively converges towards the local solution by reducing the Sum of …
data and then iteratively converges towards the local solution by reducing the Sum of …
ECKM: An improved K-means clustering based on computational geometry
A modified version of traditional k-means clustering algorithm applying computational
geometry for initialization of cluster centers has been presented in this paper. It is well …
geometry for initialization of cluster centers has been presented in this paper. It is well …
Sentiment analysis: an automatic contextual analysis and ensemble clustering approach and comparison
Product reviews are one of the most important resources to determine public sentiment. The
existing literature on review sentiment analysis mostly utilizes supervised models, which …
existing literature on review sentiment analysis mostly utilizes supervised models, which …
Efficient -Means++ Approximation with MapReduce
k-means is undoubtedly one of the most popular clustering algorithms owing to its simplicity
and efficiency. However, this algorithm is highly sensitive to the chosen initial centers and …
and efficiency. However, this algorithm is highly sensitive to the chosen initial centers and …
Deterministic initialization of the k-means algorithm using hierarchical clustering
K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately,
due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of …
due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of …
Linear, deterministic, and order-invariant initialization methods for the k-means clustering algorithm
Over the past five decades, k-means has become the clustering algorithm of choice in many
application domains primarily due to its simplicity, time/space efficiency, and invariance to …
application domains primarily due to its simplicity, time/space efficiency, and invariance to …
Phenomap** for classification of doxorubicin-induced cardiomyopathy in rats
Cardiomyopathy resistant to treatment is the most serious adverse effect of doxorubicin
(dox). The mechanisms of dox-induced cardiomyopathy (DCM) have been extensively …
(dox). The mechanisms of dox-induced cardiomyopathy (DCM) have been extensively …
Initializing FWSA K-Means With Feature Level Constraints
Z He - IEEE Access, 2022 - ieeexplore.ieee.org
Weighted K-Means (WKM) algorithms are increasingly important with the increase of data
dimension. WKM faces an initialization problem that is more complicated than K-Means' …
dimension. WKM faces an initialization problem that is more complicated than K-Means' …
Improving K-mean method by finding initial centroid points
A Aslam, U Qamar, RA Khan… - 2020 22nd International …, 2020 - ieeexplore.ieee.org
The paper is concerned with Improving k-Mean Algorithm in terms of accuracy by selecting
the best initial seed points based on the provided k value. This paper presents two modified …
the best initial seed points based on the provided k value. This paper presents two modified …