Results 31 to 40 of about 2,131,360 (300)
A Review of Quantum-Inspired Metaheuristic Algorithms for Automatic Clustering
Mathematics, 2023 In real-world scenarios, identifying the optimal number of clusters in a dataset is a difficult task due to insufficient knowledge. Therefore, the indispensability of sophisticated automatic clustering algorithms for this purpose has been contemplated by
Alokananda Dey +7 more
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Speaker segmentation and clustering [PDF]
, 2008 07.08.13 KB. Ok to add the accepted version to Spiral, Elsevier says ok whlile mandate not enforced.This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding
Kotti, Margarita +5 more
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The utility of clusters and a Hungarian clustering algorithm
PLOS ONE, 2021 Implicit in the k–means algorithm is a way to assign a value, or utility, to a cluster of points. It works by taking the centroid of the points and the value of the cluster is the sum of distances from the centroid to each point in the cluster. The aim in this paper is to introduce an alternative way to assign a value to a cluster.
Alfred Kume, Stephen G. Walker
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A survey of kernel and spectral methods for clustering [PDF]
, 2007 Clustering algorithms are a useful tool to explore data structures and have been employed in many disciplines. The focus of this paper is the partitioning clustering problem with a special interest in two recent approaches: kernel and spectral methods ...
Masulli, F. +11 more
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Differential evolution-based transfer rough clustering algorithm
Complex & Intelligent Systems, 2023 Due to well processing the uncertainty in data, rough clustering methods have been successfully applied in many fields. However, when the capacity of the available data is limited or the data are disturbed by noise, the rough clustering algorithms always
Feng Zhao, Chaofei Wang, Hanqiang Liu
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Life, 2021 Despite the scRNA-seq analytic algorithms developed, their performance for cell clustering cannot be quantified due to the unknown “true” clusters.
Yunhe Liu +5 more
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Predicting Heart Disease Using Collaborative Clustering and Ensemble Learning Techniques
Applied Sciences, 2023 Different data types are frequently included in clinical data. Applying machine learning algorithms to mixed data can be difficult and impact the output accuracy and quality.
Amna Al-Sayed +2 more
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Fair Algorithms for Clustering
CoRR, 2019 We study the problem of finding low-cost Fair Clusterings in data where each data point may belong to many protected groups. Our work significantly generalizes the seminal work of Chierichetti et.al. (NIPS 2017) as follows. - We allow the user to specify the parameters that define fair representation. More precisely, these parameters define the maximum
Suman Kalyan Bera +3 more
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Multi-type relational clustering approaches : current state-of-the-art and new directions [PDF]
, 2008 The proliferation of multi-type relational datasets in a number of important real-world applications and the limitations resulting from the transformation of such datasets to fit propositional data mining approaches have led to the emergence of the ...
Anand, Sarabjot Singh, Li, Tao
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Co-regularized weighting multiview clustering
Journal of Algorithms & Computational Technology, 2017 This paper deals with clustering for multiview data. Multiview clustering has been a research hot spot in many domains or applications, such as information retrieval, biology, chemistry, and marketing.
Cong-Zhe You, Xiao-Jun Wu
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