An Overlapping Clustering Approach for Precision, Diversity and Novelty-Aware Recommendations

2021 ◽  
pp. 114917
Author(s):  
Chems Eddine Berbague ◽  
Nour El-islam Karabadji ◽  
Hassina Seridi ◽  
Panagiotis Symeonidis ◽  
Yannis Manolopoulos ◽  
...  
Keyword(s):  
Overlapping Clustering ◽  
Clustering Approach

scholarly journals Overlapping clustering of gene expression data using penalized weighted normalized cut

2018 ◽  
Vol 42 (8) ◽  
pp. 796-811 ◽  
Author(s):  
Sebastian J. Teran Hidalgo ◽  
Tingyu Zhu ◽  
Mengyun Wu ◽  
Shuangge Ma
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Data ◽  
Normalized Cut ◽  
Overlapping Clustering

scholarly journals eHMCOKE: an enhanced overlapping clustering algorithm for data analysis

2021 ◽  
Vol 10 (4) ◽  
pp. 2212-2222
Author(s):  
Alvincent E. Danganan ◽  
Edjie Malonzo De Los Reyes
Keyword(s):  
Data Analysis ◽  
Clustering Algorithm ◽  
Median Absolute Deviation ◽  
Accuracy Rate ◽  
Absolute Deviation ◽  
Improvement Rate ◽  
Overlapping Clustering ◽  
Overlapping Clusters ◽  
Runtime Performance

Improved multi-cluster overlapping k-means extension (IMCOKE) uses median absolute deviation (MAD) in detecting outliers in datasets makes the algorithm more effective with regards to overlapping clustering. Nevertheless, analysis of the applied MAD positioning was not considered. In this paper, the incorporation of MAD used to detect outliers in the datasets was analyzed to determine the appropriate position in identifying the outlier before applying it in the clustering application. And the assumption of the study was the size of the cluster and cluster that are close to each other can led to a higher runtime performance in terms of overlapping clusters. Therefore, additional parameters such as radius of clusters and distance between clusters are added measurements in the algorithm procedures. Evaluation was done through experimentations using synthetic and real datasets. The performance of the eHMCOKE was evaluated via F1-measure criterion, speed and percentage of improvement. Evaluation results revealed that the eHMCOKE takes less time to discover overlap clusters with an improvement rate of 22% and achieved the best performance of 91.5% accuracy rate via F1-measure in identifying overlapping clusters over the IMCOKE algorithm. These results proved that the eHMCOKE significantly outruns the IMCOKE algorithm on mosts of the test conducted.

scholarly journals Non-Exhaustive, Overlapping Clustering

2019 ◽  
Vol 41 (11) ◽  
pp. 2644-2659 ◽  
Author(s):  
Joyce Jiyoung Whang ◽  
Yangyang Hou ◽  
David F. Gleich ◽  
Inderjit S. Dhillon
Keyword(s):  
Overlapping Clustering

DDOC: Overlapping Clustering of Words for Document Classification

2004 ◽  
pp. 127-128
Author(s):  
Guillaume Cleuziou ◽  
Lionel Martin ◽  
Viviane Clavier ◽  
Christel Vrain
Keyword(s):  
Document Classification ◽  
Overlapping Clustering

Overlapping Clustering: A New Method for Product Positioning

1981 ◽  
Vol 18 (3) ◽  
pp. 310-317 ◽  
Author(s):  
Phipps Arabie ◽  
J. Douglas Carroll ◽  
Wayne DeSarbo ◽  
Jerry Wind
Keyword(s):  
Market Segmentation ◽  
Equivalence Classes ◽  
New Method ◽  
Product Positioning ◽  
Theoretical Justification ◽  
Clustering Techniques ◽  
Overlapping Clustering ◽  
Clustering Model

Most clustering techniques used in product positioning and market segmentation studies render mutually exclusive equivalence classes of the relevant products or subjects space. Such classificatory techniques are thus restricted to the extent that they preclude overlap between subsets or equivalence classes. An overlapping clustering model, ADCLUS, is described which can be used in marketing studies involving products/subjects that can belong to more than one group or cluster simultaneously. The authors provide theoretical justification for and an application of the approach, using the MAPCLUS algorithm for fitting the ADCLUS model.

Overlapping Clustering Based Technique for Scalable Uncertainty Quantification in Physical Systems

2020 ◽  
Vol 8 (3) ◽  
pp. 827-859
Author(s):  
Arpan Mukherjee ◽  
Rahul Rai ◽  
Puneet Singla ◽  
Tarunraj Singh ◽  
Abani Patra
Keyword(s):  
Uncertainty Quantification ◽  
Physical Systems ◽  
Overlapping Clustering
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