modex and seed-detective: two novel techniques for high quality clustering by using good initial seeds in k-means

Klicks: 307
ID: 205416
2015
Kostenloses PDF
Artikelqualität & Leistungskennzahlen
Gesamtqualität
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
KI-Qualitätsbewertung
Nicht analysiert
Readership in this journal
Steady

Ranked #9 of 25 articles by views in journal of heritage tourism

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
In this paper we present two clustering techniques called ModEx and Seed-Detective. ModEx is a modified version of an existing clustering technique called Ex-Detective. It addresses some limitations of Ex-Detective. Seed-Detective is a combination of ModEx and Simple K-Means. Seed-Detective uses ModEx to produce a set of high quality initial seeds that are then given as input to K-Means for producing the final clusters. The high quality initial seeds are expected to produce high quality clusters through K-Means. The performances of Seed-Detective and ModEx are compared with the performances of Ex-Detective, PAM, Simple K-Means (SK), Basic Farthest Point Heuristic (BFPH) and New Farthest Point Heuristic (NFPH). We use three cluster evaluation criteria namely F-measure, Entropy and Purity and four natural datasets that we obtain from the UCI Machine learning repository. In the datasets our proposed techniques perform better than the existing techniques in terms of F-measure, Entropy and Purity. The sign test results suggest a statistical significance of the superiority of Seed-Detective (and ModEx) over the existing techniques.
Referenzschlüssel
rahman2015journalmodex Verwenden Sie diesen Schlüssel zum automatischen Zitieren im Manuskript bei Verwendung von SciMatic Manuscript Manager oder Thesis Manager
Autoren Md Anisur Rahman, Md Zahidul Islam, Terry Bossomaier
Are you one of these authors? Claim your profile
Zeitschrift journal of heritage tourism
Jahr 2015
DOI
10.1016/j.jksuci.2014.04.002
URL
http://www.sciencedirect.com/science/article/pii/S131915781500018X
Schlüsselwörter
classification clustering k-means computer science

Zitationen

Keine Zitationen gefunden. Um eine Zitation hinzuzufügen, kontaktieren Sie den Administrator unter info@scimatic.org

Kommentare

Noch keine Kommentare. Seien Sie der Erste, der diesen Artikel kommentiert.