Partition and Hierarchical Based Clustering Techniques for Analysis of Neonatal Data

Mago, Nikhit; Shirwaikar, Rudresh Deepak; Acharya, Dinesh U; Hegde, Govardhan; Lewis, Leslie; Shivakumar, Manu

doi:10.1007/978-981-10-5146-3_32

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…Hierarchical clustering was used to cluster audio features, SC was used to evaluate the clustering results. Mago et al [19] applied clustering algorithm to neonatal clustering. According to the characteristic data of neonates, hierarchical clustering was used to cluster neonates, and SC was used to evaluate the clustering results, which could provide data for doctors to diagnose neonates with different conditions.…”

Section: Related Workmentioning

confidence: 99%

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Analysis and improvement of evaluation indexes for clustering results

Zhong¹,

Zhang²,

Jia³

2020

EAI Endorsed Transactions on Collaborative Computing

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Clustering algorithm is the main field in collaborative computing of social network. How to evaluate clustering results accurately has become a hot spot in clustering algorithm research. Commonly used evaluation indexes are SC, DBI and CHI. There are two shortcomings in the calculation of three indexes. (1) Keep the number of clusters and the objects in the cluster unchanged. When transforming the feature vector, the three indexes will change greatly; (2) Keep the feature vector and the number of clusters unchanged. When changing the objects in the cluster, the three indexes will change tinily. This shows that the three indexes unable to evaluate the clustering results very well. Therefore, based on the calculation process of the three indexes, the paper proposes new three indexes -NSC, NDBI and NCHI. Through testing on standard data sets, three new indexes can better evaluate clustering results.

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Section: Related Workmentioning

confidence: 99%

“…The larger the absolute value of the CV, the more discrete the calculated value of the index. The calculation method is as shown in formula (19):…”

Section: Data Set and Evaluation Standardmentioning

confidence: 99%

Analysis and improvement of evaluation indexes for clustering results

Zhong¹,

Zhang²,

Jia³

2020

EAI Endorsed Transactions on Collaborative Computing

View full text Add to dashboard Cite

show abstract

“…How to handle these massive and complex data has become an urgent challenge in the field of machine learning at present. Clustering is one of the most important methods in machine learning, which reasonably assigns data into a cluster by selecting a measurement method [2]. Traditional clustering methods are mainly using classical machine learning theoretical knowledge to conduct research, such as partition-based clustering methods [3] and hierarchy-based clustering methods [4], etc.…”

Section: Introductionmentioning

confidence: 99%

Hypergraph-Clustering Method Based on an Improved Apriori Algorithm

Chen,

Hu,

Wang

et al. 2023

Applied Sciences

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With the complexity and variability of data structures and dimensions, traditional clustering algorithms face various challenges. The integration of network science and clustering has become a popular field of exploration. One of the main challenges is how to handle large-scale and complex high-dimensional data effectively. Hypergraphs can accurately represent multidimensional heterogeneous data, making them important for improving clustering performance. In this paper, we propose a hypergraph-clustering method dubbed the “high-dimensional data clustering method” based on hypergraph partitioning using an improved Apriori algorithm (HDHPA). First, the method constructs a hypergraph based on the improved Apriori association rule algorithm, where frequent itemsets existing in high-dimensional data are treated as hyperedges. Then, different frequent itemsets are mined in parallel to obtain hyperedges with corresponding ranks, avoiding the generation of redundant rules and improving mining efficiency. Next, we use the dense subgraph partition (DSP) algorithm to divide the hypergraph into multiple subclusters. Finally, we merge the subclusters through dense sub-hypergraphs to obtain the clustering results. The advantage of this method lies in its use of the hypergraph model to discretize the association between data in space, which further enhances the effectiveness and accuracy of clustering. We comprehensively compare the proposed HDHPA method with several advanced hypergraph-clustering methods using seven different types of high-dimensional datasets and then compare their running times. The results show that the clustering evaluation index values of the HDHPA method are generally superior to all other methods. The maximum ARI value can reach 0.834, an increase of 42%, and the average running time is lower than other methods. All in all, HDHPA exhibits an excellent comparable performance on multiple real networks. The research results of this paper provide an effective solution for processing and analyzing large-scale network datasets and are also conducive to broadening the application range of clustering techniques.

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Partition and Hierarchical Based Clustering Techniques for Analysis of Neonatal Data

Cited by 2 publications

References 9 publications

Analysis and improvement of evaluation indexes for clustering results

Analysis and improvement of evaluation indexes for clustering results

Hypergraph-Clustering Method Based on an Improved Apriori Algorithm

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