We compare three common types of clustering algorithms for use with community data. TWINSPAN is divisive hierarchical, flexible-UPGMA is agglomerative and hierarchical, and ALOC is non-hierarchical. A balanced design ...
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We compare three common types of clustering algorithms for use with community data. TWINSPAN is divisive hierarchical, flexible-UPGMA is agglomerative and hierarchical, and ALOC is non-hierarchical. A balanced design six-factor model was used to generate 480 data sets of known characteristics. Recovery of the embedded clusters suggests that both flexible UPGMA and ALOC are significantly better than TWINSPAN. No significant difference existed between flexible UPGMA and ALOC.
This paper presents a methodology for the analysis of the linkage among the processes in an organization. The methodology has three main steps: model construction;connectivity analysis;and structure analysis. The mode...
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This paper presents a methodology for the analysis of the linkage among the processes in an organization. The methodology has three main steps: model construction;connectivity analysis;and structure analysis. The model construction step generates a linkage matrix which is used in the following steps. The connectivity analysis is based on linkage consistency and interprocess coupling metrics, which are defined for each process individually and for all the processes in the organization as a group. The clustering algorithm is based on a linkage intensity measure derived from the linkage matrix. Following a detailed description of the methodology and its metrics and algorithms, the results obtained in a medium-size software development organization are presented.
We describe the design, implementation, and preliminary evaluation of a computer system to aid clinicians in the interpretation of cranial magnetic-resonance (MR) images. The system classifies normal and pathologic ti...
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We describe the design, implementation, and preliminary evaluation of a computer system to aid clinicians in the interpretation of cranial magnetic-resonance (MR) images. The system classifies normal and pathologic tissues in a test set of MR scans with high accuracy. It also provides a simple, rapid means whereby an unassisted expert may reliably label an image with his best judgment of its histologic composition, yielding a gold-standard image; this step facilitates objective evaluation of classifier performance. This system consists of a preprocessing module; a semiautomatic, reliable procedure for obtaining objective estimates of an expert's opinion of an image's tissue composition; a classification module based on a combination of the maximum-likelihood (ML) classifier and the isodata unsupervised-clustering algorithm; and an evaluation module based on confusion-matrix generation. The algorithms for classifier evaluation and gold-standard acquisition are advances over previous methods. Furthermore, the combination of a clustering algorithm and a statistical classifier provides advantages not found in systems using either method alone.
In this paper, two formulations of the group technology problem are considered: the standard formulation and the augmented formulation. The standard formulation is based on the 0–1 machine-incidence matrix and does n...
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In this paper, two formulations of the group technology problem are considered: the standard formulation and the augmented formulation. The standard formulation is based on the 0–1 machine-incidence matrix and does not consider any costs. In the augmented formulation with each partj, cost c j is associated and the number of machines in each cell is limited to N. This formulation allows the creation of machine cells and part families with a low degree of interaction (or without any interaction, if required) by removing parts with low values of the corresponding costs from the incidence matrix. To solve these formulations, tow very efficient algorithms are presented. The cluster identification algorithm with the computational time complexity 0(2mn) finds optimal machine cells and part families provided that the machine-part incidence matrix has the block diagonal structure embedded. This appears to be the most efficient algorithm developed to date. The cost analysis algorithm is designed to solve the augmented group technology problem. Its computational complexity is 0(2 mn + nlogn). The two algorithms are illustrated in numerical examples.
A loose-pattern process approach to clustering sets consists of three main computations: loose-pattern reject option, tight-pattern classifcation, and loose-pattern assigning classes. The loose-pattern rejection is im...
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A loose-pattern process approach to clustering sets consists of three main computations: loose-pattern reject option, tight-pattern classifcation, and loose-pattern assigning classes. The loose-pattern rejection is implemented using a rule based on q nearest neighbors of each point. Two clustering methods, GLC and OUPIC, are introduced as tight-pattern clustering techniques. The decisions of loose-pattern assigning classes are related to a heuristic membership function. The function and experiments with one set is discussed.","doi":"10.1109/TPAMI.1985.4767669","publicationTitle":"IEEE Transactions on Pattern Analysis and Machine Intelligence","startPage":"366","endPage":"372","rightsLink":"http://***/AppDispatchServlet?publisherName=ieee&publication=0162-8828&title=A+Loose-Pattern+Process+Approach+to+clustering+Fuzzy+Data+Sets&isbn=&publicationDate=May+1985&author=Tao+Gu&ContentID=10.1109/TPAMI.1985.4767669&orderBeanReset=true&startPage=366&endPage=372&volumeNum=PAMI-7&issueNum=3","displayPublicationTitle":"IEEE Transactions on Pattern Analysis and Machine Intelligence","pdfPath":"/iel5/34/4767650/***","keywords":[{"type":"IEEE Keywords","kwd":["Fuzzy sets","Kernel","Random variables","algorithm design and analysis","Convergence","Fast Fourier transforms","Sorting","clustering algorithms","Probability","Statistics"]},{"type":"Author Keywords ","kwd":["membership function","Classification","clustering algorithm","fuzzy discrimination","fuzzy set"]}],"allowComments":false,"pubLink":"/xpl/***?punumber=34","issueLink":"/xpl/***?isnumber=4767650","standardTitle":"A Loose-Pattern Process Approach to clustering Fuzzy Data Sets
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