Fuzzy clustering with volume prototypes and adaptive cluster merging

Two extensions to the objective function-based fuzzy clustering are proposed. First, the (point) prototypes are extended to hypervolumes, whose size can be fixed or can be determined automatically from the data being clustered. It is shown that clustering with hypervolume prototypes can be formulated as the minimization of an objective function. Second, a heuristic cluster merging step is introduced where the similarity among the clusters is assessed during optimization. Starting with an overestimation of the number of clusters in the data, similar clusters are merged in order to obtain a suitable partitioning. An adaptive threshold for merging is proposed. The extensions proposed are applied to Gustafson–Kessel and fuzzy c-means algorithms, and the resulting extended algorithm is given. The properties of the new algorithm are illustrated by various examples

Extended Fuzzy Clustering Algorithms

Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. Ithas been applied successfully in various fields including finance and marketing. Despitethe successful applications, there are a number of issues that must be dealt with in practicalapplications of fuzzy clustering algorithms. This technical report proposes two extensionsto the objective function based fuzzy clustering for dealing with these issues. First, the(point) prototypes are extended to hypervolumes whose size is determined automaticallyfrom the data being clustered. These prototypes are shown to be less sensitive to a biasin the distribution of the data. Second, cluster merging by assessing the similarity amongthe clusters during optimization is introduced. Starting with an over-estimated number ofclusters in the data, similar clusters are merged during clustering in order to obtain a suitablepartitioning of the data. An adaptive threshold for merging is introduced. The proposedextensions are applied to Gustafson-Kessel and fuzzy c-means algorithms, and the resultingextended algorithms are given. The properties of the new algorithms are illustrated invarious examples.fuzzy clustering;cluster merging;similarity;volume prototypes

Fuzzy Modeling of Client Preference in Data-Rich Marketing Environments

Advances in computational methods have led, in the world of financial services, to huge databases of client and market information. In the past decade, various computational intelligence (CI) techniques have been applied in mining this data for obtaining knowledge and in-depth information about the clients and the markets. This paper discusses the application of fuzzy clustering in target selection from large databases for direct marketing (DM) purposes. Actual data from the campaigns of a large financial services provider are used as a test case. The results obtained with the fuzzy clustering approach are compared with those resulting from the current practice of using statistical tools for target selection.fuzzy clustering;direct marketing;client segmentation;fuzzy systems

Fuzzy Modeling of Client Preference in Data-Rich Marketing Environments

Advances in computational methods have led, in the world of financial services, to huge databases of client and market information. In the past decade, various computational intelligence (CI) techniques have been applied in mining this data for obtaining knowledge and in-depth information about the clients and the markets. This paper discusses the application of fuzzy clustering in target selection from large databases for direct marketing (DM) purposes. Actual data from the campaigns of a large financial services provider are used as a test case. The results obtained with the fuzzy clustering approach are compared with those resulting from the current practice of using statistical tools for target selection

Central and Secondary Struggles in Social Interventions: The Impact of Group Relations Learning on Real Life Practices

Recently, there has been considerable research focusing on outcomes of Group Relations conferences as a unique form of adult experiential learning. Most of the focus has been on participants\u27 learning during and immediately after conferences with less attention paid to applications of learning outside conferences in participants\u27 professional and/or personal lives. The San Diego group relations/ case-in-point model is integrated into the University of San Diego\u27s graduate leadership studies program. Participants in this study included 10 individuals who had participated in this model\u27s experiential learning as teaching assistants. The methodology that was implemented, Relational Qualitative Research, synthesizes elements from several qualitative research sources. The design treated each participant as a case, but also allowed participants (functioning as co-researchers) and the researcher to jointly interpret data through a relational process. Three dimensions were used in the final analysis. First, Lacan\u27s theory of four discourses was used to identify tacit knowledge in participants\u27 mode of communication. Second, socio-structural concept of central and secondary struggles was used to discuss the influence of the class dimension, and third, distinction between therapy and analysis was used to look at whether interventions were therapeutic (i.e., adjusting to circumstances) or analytic (i.e., looking at social structure). The participants reported that the group relations learning was transformational and led to more effective social interaction in their personal and professional lives. Participants expressed psychoanalytic concepts through ordinary language so that people unfamiliar with psychoanalysis could understand their meaning. The participants used tacit knowledge to activate appropriate modes of communication dependent upon context, but could not externalize this by turning the tacit and applied knowledge into explicit and conscious knowledge. To do so would require the use of theory that is likely unknown to them. The findings show how the central antagonism is surfaced or displaced in language and thereby suggest ways learning can be redirected to address social structure. This would require an analytic stance to replace the therapeutic one that this study showed is currently predominant in this model of experiential learning

Extended Fuzzy Clustering Algorithms

Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. It has been applied successfully in various fields including finance and marketing. Despite the successful applications, there are a number of issues that must be dealt with in practical applications of fuzzy clustering algorithms. This technical report proposes two extensions to the objective function based fuzzy clustering for dealing with these issues. First, the (point) prototypes are extended to hypervolumes whose size is determined automatically from the data being clustered. These prototypes are shown to be less sensitive to a bias in the distribution of the data. Second, cluster merging by assessing the similarity among the clusters during optimization is introduced. Starting with an over-estimated number of clusters in the data, similar clusters are merged during clustering in order to obtain a suitable partitioning of the data. An adaptive threshold for merging is introduced. The proposed extensions are applied to Gustafson-Kessel and fuzzy c-means algorithms, and the resulting extended algorithms are given. The properties of the new algorithms are illustrated in various examples

Supervised fuzzy clustering for rule extraction

Abstract—This paper is concerned with the application of orthogonal transforms and fuzzy clustering to extract fuzzy rules from data. It is proposed to use the orthogonal least squares method to supervise the progress of the fuzzy clustering algorithm and remove clusters of less importance with respect to describing the data. Clustering takes place in the product space of systems inputs and outputs and each cluster corresponds to a fuzzy IF-THEN rule. By initializing the clustering with an overestimated number of clusters and subsequently remove less important ones as the clustering progresses, it is sought to obtain a suitable partition of the data in an automated manner. The approach is generally applicable to the fuzzy-means and related algorithms. It is studied in this paper for adaptive distance norm fuzzy clustering and applied to the identification of Takagi–Sugeno type rules. Both a synthetic example as well as a real-world modeling problem are considered to illustrate the working and the applicability of the algorithm. Index Terms—Clustering methods, fuzzy systems, identification, modeling, transforms. I

Feature selection for modular GA-based classification

Genetic algorithms (GAs) have been used as conventional methods for classifiers to adaptively evolve solutions for classification problems. Feature selection plays an important role in finding relevant features in classification. In this paper, feature selection is explored with modular GA-based classification. A new feature selection technique, Relative Importance Factor (RIF), is proposed to find less relevant features in the input domain of each class module. By removing these features, it is aimed to reduce the classification error and dimensionality of classification problems. Benchmark classification data sets are used to evaluate the proposed approach. The experiment results show that RIF can be used to find less relevant features and help achieve lower classification error with the feature space dimension reduced

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MasteroppgaveJUS399MAJUR-2MAJU