Semantic Approach to Cluster Validity Notion

Sivogolovko, Elena; Thalheim, Bernhard

doi:10.1007/978-3-642-32741-4_21

Cited by 2 publications

(4 citation statements)

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“…The most widespread indexes are silhouette index and Calinski-Harabasz index [Sivogolovko and Thalheim, 2013].…”

Section: Validation Of Clustering Resultsmentioning

confidence: 99%

“…Another approach to validate the clustering results is using the silhouette index. Its values shows the degree of similarity between object and cluster that he belongs to, compared to another clusters [Shi and Horvath, 2006;Soliman et al, 2017].…”

Section: Validation Of Clustering Resultsmentioning

confidence: 99%

“…Any clustering algorithm can be considered effective if the compactness hypothesis is satisfied [Shi and Horvath, 2006].…”

Section: Graph Theory Algorithms;mentioning

confidence: 99%

“…between real observations and between generated observations and their closest real neighbors [Keller et al, 1985;Sivogolovko and Thalheim, 2013]. Then the Hopkins statistics calculates as follows:…”

Section: Assessment Of Predisposition Of Data For Clusteringmentioning

confidence: 99%

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Methodology of physiography zoning using machine learning: A case study of the Black Sea

Krivoguz¹

2020

Russ. J. Earth Sci.

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Problem of area's zoning is very important and is one of the main problems of modern geographical science. Our point is to from a modern approach, based on the machine learning methods to provide zoning of any area. Key ideas of this methodology, that any distribution of factors that form any geographical system grouped around some clusters-unique zones that represents specific nature conditions. Formed methodology based on several stages-selection of data and objects for analysis, data normalization, assessment of predisposition of data for clustering, choosing the optimal number of clusters, clustering and validation of results. As an example, we tried to zone a surface layer of the Black Sea. We find that optimal number of unique zones is 3. Also, we find that the key driver of zone forming is a location of the rivers. Thus, we can say, that applying a machine learning approach in area's zoning tasks helps us increasing the quality of nature using and decision-making processes.

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“…The most widespread indexes are silhouette index and Calinski-Harabasz index [Sivogolovko and Thalheim, 2013].…”

Section: Validation Of Clustering Resultsmentioning

confidence: 99%

Section: Validation Of Clustering Resultsmentioning

confidence: 99%

“…Any clustering algorithm can be considered effective if the compactness hypothesis is satisfied [Shi and Horvath, 2006].…”

Section: Graph Theory Algorithms;mentioning

confidence: 99%

Section: Assessment Of Predisposition Of Data For Clusteringmentioning

confidence: 99%

See 2 more Smart Citations