Fuzzy Clustering Model Research Articles

A FUZZY ROUTE GUIDANCE MODEL FOR INTELLIGENT IN-VEHICLE NAVIGATION SYSTEMS

This paper presents an adaptive fuzzy clustering model that can be used to identify nature subgroups of links as well as priority memberships in a route guidance system. The fuzzy route guidance model, inspired by the fuzzy clustering technique, provides an adaptive and efficient alternative to traditional fixed costs route guidance methods. Three specific objectives underlie the presentation of the fuzzy route guidance model in this paper. The first is to describe a general overview of the in-vehicle navigation system, and the second is to introduce the fuzzy route guidance model based on adaptive fuzzy clustering and least cost problem. The third part is to demonstrate that the proposed model is able to perform route guidance in road test.

Applications of Soft Computing to Human-centered Information Systems

Applications of Soft Computing to Human-centered Information Systems

A General Fuzzy Clustering Model based on Asymmetric Aggregation Operators

We have discussed a generalized fuzzy clustering model for similarity data. In this clustering model, fuzzy aggregation operators are used to define the degree of simultaneous belongingness of a pair of objects to a cluster. In this paper, asymmetric aggregation operators are proposed to classify objects based on asymmetric similarities. Using the asymmetric aggregation operators, we get clusters which represent the asymmetric structure between objects. Moreover, the validity of this model is shown both by investigating the features of the model and through numerical applications.

A General Fuzzy Clustering Model Based on Aggregation Operators

The purpose of this paper is to propose a generalized clustering model. The structure of an observed similarity is usually unknown and complicated, so various fuzzy clustering models are required to identily the latent structure of the similarity data. Therefore, we define a general class of fuzzy clustering models, so as to represent many different structures of a similarity data. We have been discussed the additive fuzzy clustering model (Sato and Sato, 1994a, 1994b). The merits of the fuzzy clustering models are 1) the amount of computations for the identification of the models are much fewer than in a hard clustering model and 2) we obtain a suitable fitness by a using fewer number of clusters (Sato and Sato, 1994c). In the generalized clustering model, aggregation operators are used to define the degree of simultaneous belongingness of a pair of objects to a cluster. We will discuss some required conditions for the aggregation operators. T-norms are concrete examples for satisfying these conditions. Moreover, the validity of this model is shown by investigating a characteristic of the model and its numerical applications.

On A General Fuzzy Additive Clustering Model

ABSTRACTThe purpose of this article is to define a generalized structural model of similarity between a pair of objects. Applying a classification of a given data set as a structural model, we have developed an additive fuzzy clustering model.7,8 The essential merits of the additive fuzzy clustering models are 1) the amount of computations for the identification of the models are much fewer than a hard clustering model and 2) fewer number of clusters are needed to get a suitable fitness.9 This article proposes a general class of the clustering model, in which fuzzy aggregation operators are used to define a degree of simultaneous belongingness of a pair of objects to a cluster. We discuss some required conditions for the fuzzy aggregation operators. T-norm is a concrete example to satisfy the conditions. Moreover, the validity of this model is shown by investigating the characteristic feature of the model and numerical applications.

An adaptive integrated fuzzy clustering model for pattern recognition

Abstract The extension of neural net based crisp clustering algorithms to fuzzy clustering algorithms has been addressed by many researchers in recent years. However, such neuro-fuzzy clustering algorithms developed so far suffer from restrictions in identifying the actual decision boundaries among clusters with overlapping regions. These restrictions are induced by the choice of the similarity measure and representation of the clusters. An integrated adaptive fuzzy clustering (IAFC) algorithm is presented to generate improved decision boundaries by introducing a new similarity measure and by integrating the advantages of the fuzzy optimization constraint of fuzzy c-means (FCM), the control structure of adaptive resonance theory (ART-1), and a fuzzified Kohonen-type learning rule. The effect of the new similarity measure in finding nonlinear decision boundaries among closely located cluster centroids is demonstrated with computer generated data. We use the IRIS data set and a subset of the tethered satellite system simulation data set to compare the convergence rate and misclassifications resulting from IAFC algorithm with other clustering algorithms.

加法的ファジィクラスタリングモデル

本論文は, 分類対象の集合上で観測された対象間の類似度データに対する加法的ファジィクラスタリングモデルを提案しその性質について考察したものである.クラスタリングモデルにおいてクラスターとはある種の共通な性質を有する対象の部分集合として定義される.従来の加法的ハードクラスタリングモデルにおいては, 対象間の類似度はそれらが共通に属すクラスターに付けられた重さの和として表現される.したがって, このモデルによって対象間の類似度の構造を説明するためには類似度のもつ潜在次元数とクラスターの個数との関係から多数のクラスターを必要とする.本論文では, ファジィクラスター(分類対象の集合上のファジィ部分集合で表現されるクラスターをここではファジィクラスターと呼ぶ)の概念を加法的クラスタリングモデルに導入することによって少数のクラスターで類似性の構造が説明可能となることを示し, この概念に基づく3つのモデル, 単純加法的, 重複を考慮した加法的, 類似度の順序のみを考慮した加法的ファジィクラスタリングモデルを提案する.これらのモデルの考察から, 加法的ファジィクラスタリングモデルにおいては, 少数のクラスターでより詳細なクラスターの性質が抽出可能となることが示される.さらに, 従来のハードクラスタリングの場合と比較して, 帰属度がファジィグレードで表現され, 帰属度の値のとり得る自由度が増加することにより, 類似度の構造とクラスターとの関連がより明確に表現できる.