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Pregled bibliografske jedinice broj: 589890

Integrating quantitative attributes in hierarchical clustering of transactional data


Vranić, Mihaela; Pintar, Damir; Skočir, Zoran
Integrating quantitative attributes in hierarchical clustering of transactional data // Lecture Notes in Artificial Intelligence, 7327 (2012), 94-103 doi:10.1007/978-3-642-30947-2_13 (međunarodna recenzija, članak, znanstveni)


Naslov
Integrating quantitative attributes in hierarchical clustering of transactional data

Autori
Vranić, Mihaela ; Pintar, Damir ; Skočir, Zoran

Izvornik
Lecture Notes in Artificial Intelligence (0302-9743) 7327 (2012); 94-103

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Transactional Data; Hierarchical Clustering; Quantitative Attributes; Distance Measures; Retail Data

Sažetak
Appropriate data mining exploration methods can reveal valuable but hidden information in today's large quantities of transac- tional data. While association rules generation is commonly used for transactional data analysis, clustering is rather rarely used for analysis of this type of data. In this paper we provide adaptations of parameters related to association rules generation so they can be used to represent distance. Furthermore, we integrate goal-oriented quantitative attributes in distance measure formulation to increase the quality of gained results and streamline the decision making process. As a proof of concept, newly developed measures are tested and results are discussed both on a refer- ent dataset as well as a large real-life retail dataset.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo



POVEZANOST RADA


Projekt / tema
036-0362027-1638 - Umrežena ekonomija (Zoran Skočir, )

Ustanove
Fakultet elektrotehnike i računarstva, Zagreb

Časopis indeksira:


  • Scopus


Uključenost u ostale bibliografske baze podataka:


  • INSPEC
  • Scopus


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