Pattern Classification for Mixed Feature-Type Symbolic Data Using Supervised Hierarchical Conceptual Clustering

Manabu Ichino () and Hiroyuki Yaguchi
Additional contact information
Manabu Ichino: School of Science and Engineering, Tokyo Denki University, Hatoyama 350-0394, Saitama, Japan
Hiroyuki Yaguchi: School of Science and Engineering, Tokyo Denki University, Hatoyama 350-0394, Saitama, Japan

Stats, 2025, vol. 8, issue 3, 1-16

Abstract: This paper describes a region-oriented method of pattern classification based on the Cartesian system model (CSM), a mathematical model that allows manipulating mixed feature-type symbolic data. We use the supervised hierarchical conceptual clustering to generate class regions for respective pattern class based on the evaluation of the generality of the regions and the separability of the regions against other classes in each clustering step. We can easily find the robustly informative features to describe each pattern class against other pattern classes. Some examples show the effectiveness of the proposed method.

Keywords: classification; supervised feature selection; hierarchical conceptual clustering; generality; separability; Cartesian system model; symbolic data (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2571-905X/8/3/76/pdf (application/pdf)
https://www.mdpi.com/2571-905X/8/3/76/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jstats:v:8:y:2025:i:3:p:76-:d:1732351

Access Statistics for this article

Stats is currently edited by Mrs. Minnie Li

More articles in Stats from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().