scholarly journals A Hierarchical Clustering Algorithm Based on Silhouette Index for Cancer Subtype Discovery from Omics Data

2018 ◽  
Author(s):  
N. Nidheesh ◽  
K.A. Abdul Nazeer ◽  
P.M. Ameer
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Agglomerative Clustering ◽  
Cluster Validity ◽  
Cluster Validity Index ◽  
Number Of Clusters ◽  
Silhouette Index ◽  
Cancer Subtype ◽  
Hierarchical Agglomerative Clustering ◽  
Hierarchical Clustering Algorithm

AbstractCancer subtype discovery fromomicsdata requires techniques to estimate the number of natural clusters in the data. Automatically estimating the number of clusters has been a challenging problem in Machine Learning. Using clustering algorithms together with internal cluster validity indexes have been a popular method of estimating the number of clusters in biomolecular data. We propose a Hierarchical Agglomerative Clustering algorithm, namedSilHAC, which can automatically estimate the number of natural clusters and can find the associated clustering solution.SilHACis parameterless. We also present two hybrids ofSilHACwithSpectral ClusteringandK-Meansrespectively as components.SilHACand the hybrids could find reasonable estimates for the number of clusters and the associated clustering solution when applied to a collection of cancer gene expression datasets. The proposed methods are better alternatives to the ‘clustering algorithm - internal cluster validity index’ pipelines for estimating the number of natural clusters.

scholarly journals Towards Expert-Inspired Automatic Criterion to Cut a Dendrogram for Real-Industrial Applications

2021 ◽  
Author(s):  
Shikha Suman ◽  
Ashutosh Karna ◽  
Karina Gibert
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Computational Cost ◽  
Real Life ◽  
Ground Truth ◽  
Industrial Applications ◽  
Underlying Structure ◽  
Cluster Validity ◽  
Cluster Validity Index ◽  
Number Of Clusters

Hierarchical clustering is one of the most preferred choices to understand the underlying structure of a dataset and defining typologies, with multiple applications in real life. Among the existing clustering algorithms, the hierarchical family is one of the most popular, as it permits to understand the inner structure of the dataset and find the number of clusters as an output, unlike popular methods, like k-means. One can adjust the granularity of final clustering to the goals of the analysis themselves. The number of clusters in a hierarchical method relies on the analysis of the resulting dendrogram itself. Experts have criteria to visually inspect the dendrogram and determine the number of clusters. Finding automatic criteria to imitate experts in this task is still an open problem. But, dependence on the expert to cut the tree represents a limitation in real applications like the fields industry 4.0 and additive manufacturing. This paper analyses several cluster validity indexes in the context of determining the suitable number of clusters in hierarchical clustering. A new Cluster Validity Index (CVI) is proposed such that it properly catches the implicit criteria used by experts when analyzing dendrograms. The proposal has been applied on a range of datasets and validated against experts ground-truth overcoming the results obtained by the State of the Art and also significantly reduces the computational cost.

Analysis of Incremental Cluster Validity for Big Data Applications

2018 ◽  
Vol 26 (Suppl. 2) ◽  
pp. 47-62 ◽  
Author(s):  
Omar A. Ibrahim ◽  
Yiqing Wang ◽  
James M. Keller
Keyword(s):  
Clustering Algorithm ◽  
Distance Measure ◽  
Dimensional Space ◽  
Clustering Algorithms ◽  
Internal Validity ◽  
Streaming Data ◽  
Cluster Validity ◽  
Cluster Validity Index ◽  
Online Clustering ◽  
Validity Indices

Online clustering has attracted attention due to the explosion of ubiquitous continuous sensing. Streaming clustering algorithms need to look for new structures and adapt as the data evolves, such that outliers are detected, and that new emerging clusters are automatically formed. The performance of a streaming clustering algorithm needs to be monitored over time to understand the behavior of the streaming data in terms of new emerging clusters and number of outlier data points. Small datasets with 2 or 3 dimensions can be monitored by plotting the clustering results as data evolves. However, as the size and dimensions of streaming data increase, plotting the clustering result becomes unfeasible. Therefore, incremental internal Validity Indices (iCVIs) could be applied for monitoring the performance of an online clustering algorithm. In this paper, we study the internal incremental Davies-Bouldin (iDB) cluster validity index in the context of big streaming data analysis. Also, we study the effect of large number of samples on the values of the iCVI (iDB). Finally, we propose a way to project streaming data into a lower space for cases where the distance measure does not perform as expected in the high dimensional space.

Study on Information Technology with MADS — A New Data Clustering Algorithm Based on Ant Colony Optimization

2013 ◽  
Vol 859 ◽  
pp. 572-576 ◽  
Author(s):  
Yong Li Liu
Keyword(s):  
Information Technology ◽  
Ant Colony Optimization ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Ant Colony ◽  
Cluster Validity ◽  
Cluster Validity Index ◽  
Validity Index ◽  
Data Set

In the field of information technology, data clustering algorithms are widely used. In this paper, we proposed a new data clustering algorithm, named MADS, It is based on ant colony Optimization. MADS can automatically find clusters, depending on a few parameters that are not directly related to the data set. In addition, there are some existence technique was also utilized in our method, such as the density concept and cluster validity index (DB-index). The experiment results verified that MADS is able to discover clusters with varying shapes and is effective when applied to image segmentation.

Clustering of Micro-Messages Using Similarity Upper Approximation

2017 ◽  
Vol 25 (01) ◽  
pp. 53-79 ◽  
Author(s):  
Mukul Gupta ◽  
Pradeep Kumar ◽  
Bharat Bhasker
Keyword(s):  
Clustering Algorithm ◽  
State Of The Art ◽  
Clustering Algorithms ◽  
Mining Operation ◽  
Agglomerative Clustering ◽  
Upper Approximation ◽  
Affinity Propagation Clustering ◽  
Hierarchical Agglomerative Clustering ◽  
Text Content ◽  
Extract Information

Microblogging platforms like Twitter, Tumblr and Plurk have radically changed our lives. The presence of millions of people has made these platforms a preferred channel for communication. A large amount of User Generated Content, on these platforms, has attracted researchers and practitioners to mine and extract information nuggets. For information extraction, clustering is an important and widely used mining operation. This paper addresses the issue of clustering of micro-messages and corresponding users based on the text content of micro-messages that reflect their primitive interest. In this paper, we performed modification of the Similarity Upper Approximation based clustering algorithm for clustering of micro-messages. We compared the performance of the modified Similarity Upper Approximation based clustering algorithm with state-of-the-art clustering algorithms such as Partition Around Medoids, Hierarchical Agglomerative Clustering, Affinity Propagation Clustering and DBSCAN. Experiments were performed on micro-messages collected from Twitter. Experimental results show the effectiveness of the proposed algorithm.

scholarly journals Clustering of a Health Dataset Using Diagnosis Co-Occurrences

2021 ◽  
Vol 11 (5) ◽  
pp. 2373
Author(s):  
Adrien Wartelle ◽  
Farah Mourad-Chehade ◽  
Farouk Yalaoui ◽  
Jan Chrusciel ◽  
David Laplanche ◽  
...  
Keyword(s):  
Quality Indicators ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Agglomerative Clustering ◽  
Cluster Membership ◽  
Healthcare Needs ◽  
Similar Patient ◽  
Hierarchical Agglomerative Clustering ◽  
Patient Profiles ◽  
Cluster Compactness

Assessing the health profiles of populations is a crucial task to create a coherent healthcare offer. Emergency Departments (EDs) are at the core of the healthcare system and could benefit from this evaluation via an improved understanding of the healthcare needs of their population. This paper proposes a novel hierarchical agglomerative clustering algorithm based on multimorbidity analysis. The proposed approach constructs the clustering dendrogram by introducing new quality indicators based on the relative risk of co-occurrences of patient diagnoses. This algorithm enables the detection of multimorbidity patterns by merging similar patient profiles according to their common diagnoses. The multimorbidity approach has been applied to the data of the largest ED of the Aube Department (Eastern France) to cluster its patient visits. Among the 120,718 visits identified during a 24-month period, 16 clusters were identified, accounting for 94.8% of the visits, with the five most prevalent clusters representing 63.0% of them. The new quality indicators show a coherent and good clustering solution with a cluster membership of 1.81 based on a cluster compactness of 1.40 and a cluster separation of 0.77. Compared to the literature, the proposed approach is appropriate for the discovery of multimorbidity patterns and could help to develop better clustering algorithms for more diverse healthcare datasets.

scholarly journals Role of Cluster Validity Indices in Delineation of Precipitation Regions

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1372
Author(s):  
Nikhil Bhatia ◽  
Jency M. Sojan ◽  
Slobodon Simonovic ◽  
Roshan Srivastav
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Ratio Test ◽  
Cluster Validity ◽  
Number Of Clusters ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
Point Data ◽  
Optimal Number Of Clusters

The delineation of precipitation regions is to identify homogeneous zones in which the characteristics of the process are statistically similar. The regionalization process has three main components: (i) delineation of regions using clustering algorithms, (ii) determining the optimal number of regions using cluster validity indices (CVIs), and (iii) validation of regions for homogeneity using L-moments ratio test. The identification of the optimal number of clusters will significantly affect the homogeneity of the regions. The objective of this study is to investigate the performance of the various CVIs in identifying the optimal number of clusters, which maximizes the homogeneity of the precipitation regions. The k-means clustering algorithm is adopted to delineate the regions using location-based attributes for two large areas from Canada, namely, the Prairies and the Great Lakes-St Lawrence lowlands (GL-SL) region. The seasonal precipitation data for 55 years (1951–2005) is derived using high-resolution ANUSPLIN gridded point data for Canada. The results indicate that the optimal number of clusters and the regional homogeneity depends on the CVI adopted. Among 42 cluster indices considered, 15 of them outperform in identifying the homogeneous precipitation regions. The Dunn, D e t _ r a t i o and Trace( W − 1 B ) indices found to be the best for all seasons in both the regions.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

scholarly journals Hesitant Fuzzy Linguistic Agglomerative Hierarchical Clustering Algorithm and Its Application in Judicial Practice

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 370
Author(s):  
Shuangsheng Wu ◽  
Jie Lin ◽  
Zhenyu Zhang ◽  
Yushu Yang
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Agglomerative Hierarchical Clustering ◽  
Research Gaps ◽  
Judicial Practice ◽  
Linguistic Term ◽  
Clustering Effect ◽  
Hierarchical Clustering Algorithm ◽  
Fuzzy Linguistic

The fuzzy clustering algorithm has become a research hotspot in many fields because of its better clustering effect and data expression ability. However, little research focuses on the clustering of hesitant fuzzy linguistic term sets (HFLTSs). To fill in the research gaps, we extend the data type of clustering to hesitant fuzzy linguistic information. A kind of hesitant fuzzy linguistic agglomerative hierarchical clustering algorithm is proposed. Furthermore, we propose a hesitant fuzzy linguistic Boole matrix clustering algorithm and compare the two clustering algorithms. The proposed clustering algorithms are applied in the field of judicial execution, which provides decision support for the executive judge to determine the focus of the investigation and the control. A clustering example verifies the clustering algorithm’s effectiveness in the context of hesitant fuzzy linguistic decision information.

scholarly journals A novel bidirectional clustering algorithm based on local density

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Baicheng Lyu ◽  
Wenhua Wu ◽  
Zhiqiang Hu
Keyword(s):  
Clustering Algorithm ◽  
Local Density ◽  
Clustering Algorithms ◽  
Cluster Number ◽  
Denoising Method ◽  
Number Of Clusters ◽  
Data Points ◽  
Cutoff Distance ◽  
Large Clusters ◽  
Small Clusters

AbstractWith the widely application of cluster analysis, the number of clusters is gradually increasing, as is the difficulty in selecting the judgment indicators of cluster numbers. Also, small clusters are crucial to discovering the extreme characteristics of data samples, but current clustering algorithms focus mainly on analyzing large clusters. In this paper, a bidirectional clustering algorithm based on local density (BCALoD) is proposed. BCALoD establishes the connection between data points based on local density, can automatically determine the number of clusters, is more sensitive to small clusters, and can reduce the adjusted parameters to a minimum. On the basis of the robustness of cluster number to noise, a denoising method suitable for BCALoD is proposed. Different cutoff distance and cutoff density are assigned to each data cluster, which results in improved clustering performance. Clustering ability of BCALoD is verified by randomly generated datasets and city light satellite images.

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