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On the Discrepancy between Kleinberg’s Clustering Axioms and k-Means Clustering Algorithm Behavior
This paper performs an investigation of Kleinberg’s axioms (from both an intuitive and formal standpoint) as they relate to the well-known k -mean...
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A hybrid semantic recommender system based on an improved clustering
A recommender system is a model that automatically recommends some meaningful cases (such as clips/films/goods/items) to the...
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Issues in clustering algorithm consistency in fixed dimensional spaces. Some solutions for k-means
Kleinberg introduced an axiomatic system for clustering functions. Out of three axioms, he proposed, two (scale invariance and consistency) are...
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Hybrid fuzzy clustering technique to enhance the performance based on a fusion of intuitionistic modified fuzzy c-means and improved genetic algorithm
In the modern era, there is a sudden rise in data due to the wide use of the web, social networks and so on. Now, it becomes difficult to explore...
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Entropy-Based Fuzzy C-Ordered-Means Clustering Algorithm
Fuzzy C -Means is a well-known fuzzy clustering technique. Although FCM can cover the uncertainty problem by forming overlap** clusters, it involves...
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Measurement of clustering effectiveness for document collections
Clustering of the contents of a document corpus is used to create sub-corpora with the intention that they are expected to consist of documents that...
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A maximal-clique-based clustering approach for multi-observer multi-view data by using k-nearest neighbor with S-pseudo-ultrametric induced by a fuzzy similarity
Partitioning multi-view data is a recent challenge in clustering methods, which traditionally consider single-view data. In clustering techniques,...
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Cohesion and segregation in the value migration network: Evidence from network partitioning based on sector classification and clustering
Cluster structure detection of the network is a basic problem of complex network analysis. This study investigates the structure of the value...
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In-The-Limit Clustering Axioms
The paper studies the major reason for the contradictions in the Kleinberg’s axiomatic system for clustering [9]. We found that the so-called...
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Clustering by Direct Optimization of the Medoid Silhouette
The evaluation of clustering results is difficult, highly dependent on the evaluated data set and the perspective of the beholder. There are many...
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Towards continuous consistency axiom
It is shown for the first time in this paper, that Kleinberg’s (
2002 ) (self-contradictory) axiomatic system for distance-based clustering fails (that...
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Aggregated Fuzzy Equivalence Relations in Clustering Process
The aim of the work is to involve fuzzy equivalence relations and aggregation of corresponding equivalence relations in a clustering process. Namely,...
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A New Clustering Preserving Transformation for k-Means Algorithm Output
A novel clustering preserving transformation of cluster sets obtained from k-means algorithm is introduced. This transformation may be used to...
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Automated conceptual model clustering: a relator-centric approach
In recent years, there has been a growing interest in the use of reference conceptual models to capture information about complex and sensitive...
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A New Approach on Density-Based Algorithm for Clustering Dense Areas
This paper presents a new approach to density-based clustering for the identification of dense areas. In particular, the focus is on identification...
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Research on multi-source POI data fusion based on ontology and clustering algorithms
Traditional point-of-interest (POI) data are collected by professional surveying and map** organizations and are distributed in electronic maps....
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Clustering
Unsupervised learningUnsupervised learning aims to discover underlying properties and patterns from unlabeled training samples and lays the...
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A Formal Learning Theory for Three-Way Clustering
In this work, we study the theoretical properties, from the perspective of learning theory, of three-way clustering and related formalisms, such as...
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CDPS: Constrained DTW-Preserving Shapelets
The analysis of time series for clustering and classification is becoming ever more popular because of the increasingly ubiquitous nature of IoT,...
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Clustering and Identification of Core Implications
FCA exhaustively uses the notion of cluster by grou** attributes and objects and providing a solid algebraic structure to them through the concept...