Pregled bibliografske jedinice broj: 589890
Integrating quantitative attributes in hierarchical clustering of transactional data
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)
CROSBI ID: 589890 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
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
Projekti:
036-0362027-1638 - Umrežena ekonomija (Skočir, Zoran, MZO ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
Citiraj ovu publikaciju:
Časopis indeksira:
- Scopus
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- INSPEC
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