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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kalogeratos, A. | en |
| dc.contributor.author | Likas, A. | en |
| dc.date.accessioned | 2015-11-24T17:02:31Z | - |
| dc.date.available | 2015-11-24T17:02:31Z | - |
| dc.identifier.issn | 0169-023X | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/11064 | - |
| dc.rights | Default Licence | - |
| dc.subject | clustering methods | en |
| dc.subject | document clustering | en |
| dc.subject | text mining | en |
| dc.subject | term selection | en |
| dc.subject | subspace clustering | en |
| dc.subject | algorithm | en |
| dc.subject | model | en |
| dc.title | Document clustering using synthetic cluster prototypes | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | DOI 10.1016/j.datak.2010.12.002 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2011 | - |
| heal.abstract | The use of centroids as prototypes for clustering text documents with the k-means family of methods is not always the best choice for representing text clusters due to the high dimensionality, sparsity, and low quality of text data. Especially for the cases where we seek clusters with small number of objects, the use of centroids may lead to poor solutions near the bad initial conditions. To overcome this problem, we propose the idea of synthetic cluster prototype that is computed by first selecting a subset of cluster objects (instances), then computing the representative of these objects and finally selecting important features. In this spirit, we introduce the MedoidKNN synthetic prototype that favors the representation of the dominant class in a cluster. These synthetic cluster prototypes are incorporated into the generic spherical k-means procedure leading to a robust clustering method called k-synthetic prototypes (k-sp). Comparative experimental evaluation demonstrates the robustness of the approach especially for small datasets and clusters overlapping in many dimensions and its superior performance against traditional and subspace clustering methods. (c) 2010 Elsevier B.V. All rights reserved. | en |
| heal.journalName | Data & Knowledge Engineering | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Kalogeratos-2011-Document clustering.pdf | 1.69 MB | Adobe PDF | View/Open Request a copy |
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