Integrating Fuzzy C-Means and DBSCAN: A Hybrid Approach to Medical Data Mining Article Swipe
Vikas Kaduskar
,
Anurag Srivastava
,
Saif O. Husain
,
Manish Gupta
,
Vinod Patil
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.26599/fie.2025.9270055
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.26599/fie.2025.9270055
Medical data mining is crucial to gain meaningful insights from complex healthcare databases. Medical data sometimes exhibits ambiguity and overlap due to inconsistent diagnosis and varying patient situations. This work proposes a hybrid clustering strategy that combines Fuzzy C-Means (FCM) with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to tackle these challenges. FCM’s capacity to manage fuzzy memberships allows each data point to belong to many clusters with varying degrees of membership, accommodating the inherent ambiguities in medical data. With the help of the density-based clustering algorithm, the model can better identify and manage noise while detecting clusters with varying densities and shapes. The integration of the hybrid model aims to enhance patient segmentation by facilitating the identification of more complex and significant subgroups based on clinical markers. This technique improves the precision of sickness classification, leading to more customized treatment plans. Experimental validation and case studies show significant improvements in clustering quality of the model over existing methods. This leads to data that are more easily comprehended by medical professionals. The results show how the hybrid model might be a helpful tool for decision support systems and precision medicine, which would assist medical practitioners.
Related Topics
Concepts
Data mining
DBSCAN
Computer science
Fuzzy logic
Artificial intelligence
Fuzzy clustering
Canopy clustering algorithm
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.26599/fie.2025.9270055
- OA Status
- gold
- Cited By
- 1
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409122072
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4409122072Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.26599/fie.2025.9270055Digital Object Identifier
- Title
-
Integrating Fuzzy C-Means and DBSCAN: A Hybrid Approach to Medical Data MiningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-01Full publication date if available
- Authors
-
Vikas Kaduskar, Anurag Srivastava, Saif O. Husain, Manish Gupta, Vinod PatilList of authors in order
- Landing page
-
https://doi.org/10.26599/fie.2025.9270055Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.26599/fie.2025.9270055Direct OA link when available
- Concepts
-
Data mining, DBSCAN, Computer science, Fuzzy logic, Artificial intelligence, Fuzzy clustering, Canopy clustering algorithmTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
-
19Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4409122072 |
|---|---|
| doi | https://doi.org/10.26599/fie.2025.9270055 |
| ids.doi | https://doi.org/10.26599/fie.2025.9270055 |
| ids.openalex | https://openalex.org/W4409122072 |
| fwci | 9.66367325 |
| type | article |
| title | Integrating Fuzzy C-Means and DBSCAN: A Hybrid Approach to Medical Data Mining |
| biblio.issue | 1 |
| biblio.volume | 17 |
| biblio.last_page | 119 |
| biblio.first_page | 108 |
| topics[0].id | https://openalex.org/T10538 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.8716999888420105 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1710 |
| topics[0].subfield.display_name | Information Systems |
| topics[0].display_name | Data Mining Algorithms and Applications |
| is_xpac | False |
| apc_list.value | 385 |
| apc_list.currency | GBP |
| apc_list.value_usd | 472 |
| apc_paid.value | 385 |
| apc_paid.currency | GBP |
| apc_paid.value_usd | 472 |
| concepts[0].id | https://openalex.org/C124101348 |
| concepts[0].level | 1 |
| concepts[0].score | 0.6661829948425293 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[0].display_name | Data mining |
| concepts[1].id | https://openalex.org/C46576248 |
| concepts[1].level | 5 |
| concepts[1].score | 0.5975303649902344 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1114630 |
| concepts[1].display_name | DBSCAN |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.5762231945991516 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C58166 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5457366108894348 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q224821 |
| concepts[3].display_name | Fuzzy logic |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.34490466117858887 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C17212007 |
| concepts[5].level | 3 |
| concepts[5].score | 0.15097597241401672 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q5511111 |
| concepts[5].display_name | Fuzzy clustering |
| concepts[6].id | https://openalex.org/C104047586 |
| concepts[6].level | 4 |
| concepts[6].score | 0.0 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q5033439 |
| concepts[6].display_name | Canopy clustering algorithm |
| keywords[0].id | https://openalex.org/keywords/data-mining |
| keywords[0].score | 0.6661829948425293 |
| keywords[0].display_name | Data mining |
| keywords[1].id | https://openalex.org/keywords/dbscan |
| keywords[1].score | 0.5975303649902344 |
| keywords[1].display_name | DBSCAN |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.5762231945991516 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/fuzzy-logic |
| keywords[3].score | 0.5457366108894348 |
| keywords[3].display_name | Fuzzy logic |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.34490466117858887 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/fuzzy-clustering |
| keywords[5].score | 0.15097597241401672 |
| keywords[5].display_name | Fuzzy clustering |
| language | en |
| locations[0].id | doi:10.26599/fie.2025.9270055 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S101936814 |
| locations[0].source.issn | 1616-8658, 1616-8666 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1616-8658 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Fuzzy Information and Engineering |
| locations[0].source.host_organization | https://openalex.org/P4310320547 |
| locations[0].source.host_organization_name | Taylor & Francis |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320547 |
| locations[0].source.host_organization_lineage_names | Taylor & Francis |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Fuzzy Information and Engineering |
| locations[0].landing_page_url | https://doi.org/10.26599/fie.2025.9270055 |
| locations[1].id | pmh:oai:doaj.org/article:0581427a420b4d218f0ae88b5895bb99 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Fuzzy Information and Engineering, Vol 17, Iss 1, Pp 108-119 (2025) |
| locations[1].landing_page_url | https://doaj.org/article/0581427a420b4d218f0ae88b5895bb99 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5092099622 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-8925-7651 |
| authorships[0].author.display_name | Vikas Kaduskar |
| authorships[0].countries | IN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I887998513 |
| authorships[0].affiliations[0].raw_affiliation_string | Bharati Vidyapeeth (Deemed to be University) College of Engineering,Department of Electronics and Communication Engineering,Pune,India,411043 |
| authorships[0].institutions[0].id | https://openalex.org/I887998513 |
| authorships[0].institutions[0].ror | https://ror.org/0052mmx10 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I887998513 |
| authorships[0].institutions[0].country_code | IN |
| authorships[0].institutions[0].display_name | Bharati Vidyapeeth Deemed University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Vikas Kaduskar |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Bharati Vidyapeeth (Deemed to be University) College of Engineering,Department of Electronics and Communication Engineering,Pune,India,411043 |
| authorships[1].author.id | https://openalex.org/A5100745231 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-7046-405X |
| authorships[1].author.display_name | Anurag Srivastava |
| authorships[1].countries | IN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I85461943 |
| authorships[1].affiliations[0].raw_affiliation_string | Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences,Chennai,India,602105 |
| authorships[1].institutions[0].id | https://openalex.org/I85461943 |
| authorships[1].institutions[0].ror | https://ror.org/0034me914 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I85461943 |
| authorships[1].institutions[0].country_code | IN |
| authorships[1].institutions[0].display_name | Saveetha University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Anurag Srivastava |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences,Chennai,India,602105 |
| authorships[2].author.id | https://openalex.org/A5018589382 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Saif O. Husain |
| authorships[2].countries | IQ |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I3131779051 |
| authorships[2].affiliations[0].raw_affiliation_string | Islamic University of Najaf,Faculty of Technical Engineering,Department of Computer Technology Engineering,Najaf,Iraq,2975+9RM |
| authorships[2].institutions[0].id | https://openalex.org/I3131779051 |
| authorships[2].institutions[0].ror | https://ror.org/01wfhkb67 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I3131779051 |
| authorships[2].institutions[0].country_code | IQ |
| authorships[2].institutions[0].display_name | Iraqi University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Saif O. Husain |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Islamic University of Najaf,Faculty of Technical Engineering,Department of Computer Technology Engineering,Najaf,Iraq,2975+9RM |
| authorships[3].author.id | https://openalex.org/A5087575001 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-1949-2231 |
| authorships[3].author.display_name | Manish Gupta |
| authorships[3].countries | IN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I110360157 |
| authorships[3].affiliations[0].raw_affiliation_string | Lovely Professional University,Division of Research and Development,Phagwara,India,144411 |
| authorships[3].institutions[0].id | https://openalex.org/I110360157 |
| authorships[3].institutions[0].ror | https://ror.org/00et6q107 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I110360157 |
| authorships[3].institutions[0].country_code | IN |
| authorships[3].institutions[0].display_name | Lovely Professional University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Manish Gupta |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Lovely Professional University,Division of Research and Development,Phagwara,India,144411 |
| authorships[4].author.id | https://openalex.org/A5042322668 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-3328-9248 |
| authorships[4].author.display_name | Vinod Patil |
| authorships[4].countries | IN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I887998513 |
| authorships[4].affiliations[0].raw_affiliation_string | Bharati Vidyapeeth (Deemed to be University) College of Engineering,Department of Electronics and Telecommunication Engineering,Pune,India,411043 |
| authorships[4].institutions[0].id | https://openalex.org/I887998513 |
| authorships[4].institutions[0].ror | https://ror.org/0052mmx10 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I887998513 |
| authorships[4].institutions[0].country_code | IN |
| authorships[4].institutions[0].display_name | Bharati Vidyapeeth Deemed University |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Vinod Patil |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Bharati Vidyapeeth (Deemed to be University) College of Engineering,Department of Electronics and Telecommunication Engineering,Pune,India,411043 |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.26599/fie.2025.9270055 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Integrating Fuzzy C-Means and DBSCAN: A Hybrid Approach to Medical Data Mining |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10538 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.8716999888420105 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1710 |
| primary_topic.subfield.display_name | Information Systems |
| primary_topic.display_name | Data Mining Algorithms and Applications |
| related_works | https://openalex.org/W4391375266, https://openalex.org/W2899084033, https://openalex.org/W2748952813, https://openalex.org/W3163639875, https://openalex.org/W2364999035, https://openalex.org/W4226497289, https://openalex.org/W2966681114, https://openalex.org/W4386078164, https://openalex.org/W2553918434, https://openalex.org/W2369356834 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.26599/fie.2025.9270055 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S101936814 |
| best_oa_location.source.issn | 1616-8658, 1616-8666 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1616-8658 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Fuzzy Information and Engineering |
| best_oa_location.source.host_organization | https://openalex.org/P4310320547 |
| best_oa_location.source.host_organization_name | Taylor & Francis |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320547 |
| best_oa_location.source.host_organization_lineage_names | Taylor & Francis |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Fuzzy Information and Engineering |
| best_oa_location.landing_page_url | https://doi.org/10.26599/fie.2025.9270055 |
| primary_location.id | doi:10.26599/fie.2025.9270055 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S101936814 |
| primary_location.source.issn | 1616-8658, 1616-8666 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1616-8658 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Fuzzy Information and Engineering |
| primary_location.source.host_organization | https://openalex.org/P4310320547 |
| primary_location.source.host_organization_name | Taylor & Francis |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320547 |
| primary_location.source.host_organization_lineage_names | Taylor & Francis |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Fuzzy Information and Engineering |
| primary_location.landing_page_url | https://doi.org/10.26599/fie.2025.9270055 |
| publication_date | 2025-03-01 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4367187243, https://openalex.org/W2903590249, https://openalex.org/W1990368529, https://openalex.org/W4367591795, https://openalex.org/W4383100501, https://openalex.org/W4291150331, https://openalex.org/W4213418104, https://openalex.org/W4313256145, https://openalex.org/W2888914817, https://openalex.org/W4321488553, https://openalex.org/W3084250793, https://openalex.org/W4389891116, https://openalex.org/W4285118743, https://openalex.org/W2128382462, https://openalex.org/W2949515600, https://openalex.org/W4307636017, https://openalex.org/W3168487770, https://openalex.org/W2929848775, https://openalex.org/W1995450389 |
| referenced_works_count | 19 |
| abstract_inverted_index.a | 31, 181 |
| abstract_inverted_index.be | 180 |
| abstract_inverted_index.by | 115, 169 |
| abstract_inverted_index.in | 77, 151 |
| abstract_inverted_index.is | 3 |
| abstract_inverted_index.of | 44, 71, 83, 106, 119, 134, 154 |
| abstract_inverted_index.on | 126 |
| abstract_inverted_index.to | 5, 21, 49, 55, 63, 65, 111, 138, 162 |
| abstract_inverted_index.The | 104, 172 |
| abstract_inverted_index.and | 18, 24, 93, 102, 122, 145, 188 |
| abstract_inverted_index.are | 165 |
| abstract_inverted_index.can | 90 |
| abstract_inverted_index.due | 20 |
| abstract_inverted_index.for | 184 |
| abstract_inverted_index.how | 175 |
| abstract_inverted_index.the | 74, 81, 84, 88, 107, 117, 132, 155, 176 |
| abstract_inverted_index.This | 28, 129, 160 |
| abstract_inverted_index.With | 80 |
| abstract_inverted_index.aims | 110 |
| abstract_inverted_index.case | 146 |
| abstract_inverted_index.data | 1, 14, 61, 163 |
| abstract_inverted_index.each | 60 |
| abstract_inverted_index.from | 9 |
| abstract_inverted_index.gain | 6 |
| abstract_inverted_index.help | 82 |
| abstract_inverted_index.many | 66 |
| abstract_inverted_index.more | 120, 139, 166 |
| abstract_inverted_index.over | 157 |
| abstract_inverted_index.show | 148, 174 |
| abstract_inverted_index.that | 35, 164 |
| abstract_inverted_index.tool | 183 |
| abstract_inverted_index.with | 40, 46, 68, 99 |
| abstract_inverted_index.work | 29 |
| abstract_inverted_index.(FCM) | 39 |
| abstract_inverted_index.Fuzzy | 37 |
| abstract_inverted_index.Noise | 47 |
| abstract_inverted_index.based | 125 |
| abstract_inverted_index.data. | 79 |
| abstract_inverted_index.fuzzy | 57 |
| abstract_inverted_index.leads | 161 |
| abstract_inverted_index.might | 179 |
| abstract_inverted_index.model | 89, 109, 156, 178 |
| abstract_inverted_index.noise | 95 |
| abstract_inverted_index.point | 62 |
| abstract_inverted_index.these | 51 |
| abstract_inverted_index.which | 191 |
| abstract_inverted_index.while | 96 |
| abstract_inverted_index.would | 192 |
| abstract_inverted_index.allows | 59 |
| abstract_inverted_index.assist | 193 |
| abstract_inverted_index.belong | 64 |
| abstract_inverted_index.better | 91 |
| abstract_inverted_index.easily | 167 |
| abstract_inverted_index.hybrid | 32, 108, 177 |
| abstract_inverted_index.manage | 56, 94 |
| abstract_inverted_index.mining | 2 |
| abstract_inverted_index.plans. | 142 |
| abstract_inverted_index.tackle | 50 |
| abstract_inverted_index.C-Means | 38 |
| abstract_inverted_index.FCM’s | 53 |
| abstract_inverted_index.Medical | 0, 13 |
| abstract_inverted_index.Spatial | 42 |
| abstract_inverted_index.complex | 10, 121 |
| abstract_inverted_index.crucial | 4 |
| abstract_inverted_index.degrees | 70 |
| abstract_inverted_index.enhance | 112 |
| abstract_inverted_index.helpful | 182 |
| abstract_inverted_index.leading | 137 |
| abstract_inverted_index.medical | 78, 170, 194 |
| abstract_inverted_index.overlap | 19 |
| abstract_inverted_index.patient | 26, 113 |
| abstract_inverted_index.quality | 153 |
| abstract_inverted_index.results | 173 |
| abstract_inverted_index.shapes. | 103 |
| abstract_inverted_index.studies | 147 |
| abstract_inverted_index.support | 186 |
| abstract_inverted_index.systems | 187 |
| abstract_inverted_index.varying | 25, 69, 100 |
| abstract_inverted_index.(DBSCAN) | 48 |
| abstract_inverted_index.capacity | 54 |
| abstract_inverted_index.clinical | 127 |
| abstract_inverted_index.clusters | 67, 98 |
| abstract_inverted_index.combines | 36 |
| abstract_inverted_index.decision | 185 |
| abstract_inverted_index.exhibits | 16 |
| abstract_inverted_index.existing | 158 |
| abstract_inverted_index.identify | 92 |
| abstract_inverted_index.improves | 131 |
| abstract_inverted_index.inherent | 75 |
| abstract_inverted_index.insights | 8 |
| abstract_inverted_index.markers. | 128 |
| abstract_inverted_index.methods. | 159 |
| abstract_inverted_index.proposes | 30 |
| abstract_inverted_index.sickness | 135 |
| abstract_inverted_index.strategy | 34 |
| abstract_inverted_index.ambiguity | 17 |
| abstract_inverted_index.densities | 101 |
| abstract_inverted_index.detecting | 97 |
| abstract_inverted_index.diagnosis | 23 |
| abstract_inverted_index.medicine, | 190 |
| abstract_inverted_index.precision | 133, 189 |
| abstract_inverted_index.sometimes | 15 |
| abstract_inverted_index.subgroups | 124 |
| abstract_inverted_index.technique | 130 |
| abstract_inverted_index.treatment | 141 |
| abstract_inverted_index.Clustering | 43 |
| abstract_inverted_index.algorithm, | 87 |
| abstract_inverted_index.clustering | 33, 86, 152 |
| abstract_inverted_index.customized | 140 |
| abstract_inverted_index.databases. | 12 |
| abstract_inverted_index.healthcare | 11 |
| abstract_inverted_index.meaningful | 7 |
| abstract_inverted_index.validation | 144 |
| abstract_inverted_index.ambiguities | 76 |
| abstract_inverted_index.challenges. | 52 |
| abstract_inverted_index.integration | 105 |
| abstract_inverted_index.membership, | 72 |
| abstract_inverted_index.memberships | 58 |
| abstract_inverted_index.significant | 123, 149 |
| abstract_inverted_index.situations. | 27 |
| abstract_inverted_index.Applications | 45 |
| abstract_inverted_index.Experimental | 143 |
| abstract_inverted_index.comprehended | 168 |
| abstract_inverted_index.facilitating | 116 |
| abstract_inverted_index.improvements | 150 |
| abstract_inverted_index.inconsistent | 22 |
| abstract_inverted_index.segmentation | 114 |
| abstract_inverted_index.Density-Based | 41 |
| abstract_inverted_index.accommodating | 73 |
| abstract_inverted_index.density-based | 85 |
| abstract_inverted_index.identification | 118 |
| abstract_inverted_index.practitioners. | 195 |
| abstract_inverted_index.professionals. | 171 |
| abstract_inverted_index.classification, | 136 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.93196169 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |