An Entropy based Method for Overlapping Subspace Clustering

Puri, Charu; Kumar, Naveen

doi:10.1016/j.procs.2017.11.370

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Figure 4 shows that this algorithm cannot identify better cluster groups or centroids when applied to the Dataset. This algorithm uses GKFK with entropy regularization [25]. Figure 5 shows the clustering procedure used to the specified Dataset if standardization is stand=1.…”

Section: Cluster Formation Using Variant Fuzzy Clustering Algorithmsmentioning

confidence: 99%

Validation on selected breast cancer drugs of physicochemical features by using machine learning models

Gupta,

Krishna,

Murthy

et al. 2024

IJPHS

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Breast cancer is one of the leading causes of death among females today. The elbow approach determines the ideal number of clusters after determining that the Dataset is highly cluster able with the Hopkins statistic. Three distinct groups with distinct differences were produced using the dataset's proposed expectation maximization fuzzy k-means clustering algorithm (PEMFKM). Different fuzzy clustering techniques, such as fuzzy k-means (FKM), fuzzy k-means with entropy (FKM.ENT), fuzzy k-means with entropy and noise (FKM.ENT.NOISE), Gustafson and Kessel - like fuzzy k-means (FKM.GK), Gustafson and Kessel - like fuzzy k-means with entropy regularization (FKM.GK.ENT), Gustafson and Kessel - like fuzzy kmeans with entropy regularization and noise (FKM.GK.ENT.NOISE), and PEMFKM, are evaluated. The partition coefficient (PC), partition entropy (PE), and Modified partition coefficient index (MPC) index values are better for FKM.GK than the suggested PEMFKM method. When compared to the FKM.GK method, the index values for the proposed PEMFKM algorithm have superior results for the parameters Silhouette (SIL), Xie and Beni index (XB), and fuzzy silhouette index (SIL.F). The results shows that the PEMFKM algorithm will provide better clusters and that the drugs in a given cluster may be combined for use in combination therapy for breast cancer treatment.

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Section: Cluster Formation Using Variant Fuzzy Clustering Algorithmsmentioning

confidence: 99%

Validation on selected breast cancer drugs of physicochemical features by using machine learning models

Gupta,

Krishna,

Murthy

et al. 2024

IJPHS

View full text Add to dashboard Cite

show abstract

“…They claim that it can be applied for solving problems of frequent itemset mining and subspace clustering to enhance e±ciency and accuracy. Puri and Kumar (2017) present a subspace clustering method based on entropy and modi¯cation of Gustafson-Kessel clustering in which each cluster and its attributes are represented through gradation in memberships. A Transformation Invariant Subspace Clustering framework is proposed by Qi et al (2016), in which data samples are aligned so that they become highly correlated and then better similarity matrix can be obtained.…”

Section: Recent Related Workmentioning

confidence: 99%

A Novel Parameter-Light Subspace Clustering Technique Based on Single Linkage Method

Kelkar

Rodd²,

Kulkarni

2019

J. Info. Know. Mgmt.

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Subspace clustering is a challenging high-dimensional data mining task. There have been several approaches proposed in the literature to identify clusters in subspaces, however their performance and quality is highly affected by input parameters. A little research is done so far on identifying proper parameter values automatically. Other observed drawbacks are requirement of multiple database scans resulting into increased demand for computing resources and generation of many redundant clusters. Here, we propose a parameter light subspace clustering method for numerical data hereafter referred to as CLUSLINK. The algorithm is based on single linkage clustering method and works in bottom up, greedy fashion. The only input user has to provide is how coarse or fine the resulting clusters should be, and if not given, the algorithm operates with default values. The empirical results obtained over synthetic and real benchmark datasets show significant improvement in terms of accuracy and execution time.

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Entropy-Based Fuzzy C-Ordered-Means Clustering Algorithm

Moradi,

Hamidzadeh

2023

New Gener. Comput.

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An Entropy based Method for Overlapping Subspace Clustering

Cited by 4 publications

References 14 publications

Validation on selected breast cancer drugs of physicochemical features by using machine learning models

Validation on selected breast cancer drugs of physicochemical features by using machine learning models

A Novel Parameter-Light Subspace Clustering Technique Based on Single Linkage Method

Entropy-Based Fuzzy C-Ordered-Means Clustering Algorithm

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