On-line hierarchical clustering
Pattern Recognition LettersPublished 1 December 1998
Yasser El-Sonbaty, Mohamed A. Ismail
Citations33
SJR quartileQ1
SJR score1.00
SNIP1.43
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
The results of the application of the new algorithm on real and synthetic data and also using simulation experiments, show that the new technique is quite efficient and, in many respects, superior to traditional off-line hierarchical methods.
Abstract
Most of the techniques used in the literature for hierarchical clustering are based on off-line operation. The main contribution of this paper is to propose a new algorithm for on-line hierarchical clustering by finding the nearest k objects to each introduced object so far and these nearest k objects are continuously updated by the arrival of a new object. By final object, we have the objects and their nearest k objects which are sorted to produce the hierarchical dendogram. The results of the application of the new algorithm on real and synthetic data and also using simulation experiments, show that the new technique is quite efficient and, in many respects, superior to traditional off-line hierarchical methods.
Keywords
Computer Science
Machine LearningKnowledge acquisition via incremental conceptual clustering
1,081 Citations1987Douglas Fisher
COBWEB is a conceptual clustering system that organizes data so as to maximize inference ability, and is incremental and computationally economical, and thus can be flexibly applied in a variety of domains.
Models of Incremental Concept Formation
527 Citations1988John H. Gennari, Pat Langley +1 more
Artificial IntelligenceModels of incremental concept formation
457 Citations1989John H. Gennari, Pat Langley +1 more
This paper reviews three previous models of incremental concept formation and presents CLASSIT, a model that extends these earlier systems that integrate the process of recognition and learning and also presents some empirical studies of its behavior under a variety of conditions.
IEEE Transactions on Pattern Analysis and Machine IntelligenceAutomated Construction of Classifications: Conceptual Clustering Versus Numerical Taxonomy
317 Citations1983Ryszard S. Michalski, Robert E. Stepp
A method for automated construction of classifications called conceptual clustering is described and compared to methods used in numerical taxonomy, in which descriptive concepts are conjunctive statements involving relations on selected object attributes and optimized according to an assumed global criterion of clustering quality.
Pattern RecognitionSymbolic clustering using a new dissimilarity measure
301 Citations1991K. Chidananda Gowda, Edwin Diday
A new dissimilarity measure, based on “position”, “span” and “content” of symbolic objects is proposed for symbolic clustering, and the results of the application of the algorithm on numeric data of known number of classes are described first to show the efficacy of the method.
Machine LearningExperiments with Incremental Concept Formation: UNIMEM
289 Citations1987Michael Lebowitz
UNIMEM is a robust program that can be run on many domains with real-world problem characteristics such as uncertainty, incompleteness, and large numbers of examples, including the automatic creation of non-disjoint concept hierarchies that are evaluated over time.
Pattern Recognition LettersA conceptual version of the K-means algorithm
267 Citations1995Henri Ralambondrainy
A hybrid numericsymbolic method that integrates an extended version of the K-means algorithm for cluster determination and a complementary conceptual characterization algorithm for clusters description is proposed.
IEEE Transactions on Systems Man and CyberneticsSymbolic clustering using a new similarity measure
204 Citations1992K.C. Gowda, Edwin Diday
A hierarchical, agglomerative, symbolic clustering methodology based on a similarity measure that takes into consideration the position, span, and content of symbolic objects is proposed and is capable of discerning clusters in data sets made up of numeric as well as symbolic objects consisting of different types and combinations of qualitative and quantitative feature values.
IEEE Transactions on Fuzzy SystemsFuzzy clustering for symbolic data
136 Citations1998Yasser El-Sonbaty, Mohamed A. Ismail
A fuzzy symbolic c-means algorithm is introduced as an application of applying and testing the proposed algorithm on real and synthetic data sets and the results show that the new technique is quite efficient and superior to traditional methods of hierarchical nature.
Pattern RecognitionDivisive clustering of symbolic objects using the concepts of both similarity and dissimilarity
88 Citations1995K. Chidananda Gowda, T. Ravi
A divisive clustering algorithm which makes use of both similarity and dissimilarity is proposed and the results obtained are compared with other methods.
IEEE Transactions on Pattern Analysis and Machine IntelligenceConceptual Clustering in Knowledge Organization
81 Citations1985Yizong Cheng, King-Sun Fu
Concepts used in knowledge description are divided into tangible ones and intermediate ones depending on whether or not they appear in the input or the output of the system.
General Metrics For Mixed Features The Cartesian Space Theory For Pattern Recognition
80 Citations2005Manabu Ichino
General distance functions for mixed feature variables are defined based on the Cartesian space (U/sup (d)/, +,x) which is the new mathematical model for pattern recognition.