Hybrid Cluster Validation Techniques

Gajawada, Satish; Toshniwal, Durga

doi:10.1007/978-3-642-30111-7_25

Cited by 7 publications

(9 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section we present the results obtained for some synthetic datasets with a hybrid cluster validation index proposed in [15]. We also present the results obtained for Wisconsin breast cancer data [32] with proposed method.…”

Section: Resultsmentioning

confidence: 99%

“…External index has been calculated using labelled data part of the dataset. Results have been obtained using Dunn index and hybrid Dunn-CEGR index [15] with equal weights on two datasets. Results obtained are discussed below.…”

Section: Resultsmentioning

confidence: 99%

“…Gain obtained from Gini index can be used for finding impurity of certain split in decision trees [14]. This gain criterion can be used for external cluster validation [15]. In the proposed hybrid SCQE-Gini Gain index, the evaluation of clustering solution is based on SCQE index and Gini Gain index.…”

Section: Proposed Workmentioning

confidence: 99%

“…Hybrid cluster validation indices are based on internal and external cluster validation indices. These indices use the available external information in the validation process in addition to intrinsic information present in the data [15].…”

Section: Introductionmentioning

confidence: 99%

“…Gini index, Entropy index and Classification error index can be used in the gain criterion defined in [14] to get Gini gain index, Information gain index and Classification error gain index respectively. The quality of clustering solutions can be evaluated using these impurity measures [15].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution

Gajawada¹

2012

IJSEA

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Proposed Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution

Gajawada¹

2012

IJSEA

Self Cite

View full text Add to dashboard Cite

show abstract

Research proposal content extraction using natural language processing and semi-supervised clustering: A demonstration and comparative analysis

Knisely

Pavliscsak

2023

Scientometrics

View full text Add to dashboard Cite

Funding institutions often solicit text-based research proposals to evaluate potential recipients. Leveraging the information contained in these documents could help institutions understand the supply of research within their domain. In this work, an end-to-end methodology for semi-supervised document clustering is introduced to partially automate classification of research proposals based on thematic areas of interest. The methodology consists of three stages: (1) manual annotation of a document sample; (2) semi-supervised clustering of documents; (3) evaluation of cluster results using quantitative metrics and qualitative ratings (coherence, relevance, distinctiveness) by experts. The methodology is described in detail to encourage replication and is demonstrated on a real-world data set. This demonstration sought to categorize proposals submitted to the US Army Telemedicine and Advanced Technology Research Center (TATRC) related to technological innovations in military medicine. A comparative analysis of method features was performed, including unsupervised vs. semi-supervised clustering, several document vectorization techniques, and several cluster result selection strategies. Outcomes suggest that pretrained Bidirectional Encoder Representations from Transformers (BERT) embeddings were better suited for the task than older text embedding techniques. When comparing expert ratings between algorithms, semi-supervised clustering produced coherence ratings ~ 25% better on average compared to standard unsupervised clustering with negligible differences in cluster distinctiveness. Last, it was shown that a cluster result selection strategy that balances internal and external validity produced ideal results. With further refinement, this methodological framework shows promise as a useful analytical tool for institutions to unlock hidden insights from untapped archives and similar administrative document repositories. Supplementary Information The online version contains supplementary material available at 10.1007/s11192-023-04689-3.

show abstract

Device personalization for heterogeneous populations: leveraging physician expertise and national population data to identify medical device patient user groups

Knisely

Vaughn-Cooke

Wagner

et al. 2021

User Model User-Adap Inter

View full text Add to dashboard Cite

Hybrid Cluster Validation Techniques

Cited by 7 publications

References 14 publications

Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution

Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution

Research proposal content extraction using natural language processing and semi-supervised clustering: A demonstration and comparative analysis

Device personalization for heterogeneous populations: leveraging physician expertise and national population data to identify medical device patient user groups

Contact Info

Product

Resources

About