Given the increasing demand for blood smear analysis in the Hematology department of Oran (Algeria) Hospital and worldwide, in the literature there are some methods directed to the automation of this important problem...
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Given the increasing demand for blood smear analysis in the Hematology department of Oran (Algeria) Hospital and worldwide, in the literature there are some methods directed to the automation of this important problem. This paper presents a state-of-art about the used clustering methods.
clustering is one of the important techniques in data mining. The objective of clustering is to group objects into clusters such that objects within a cluster are more similar to each other than objects in different c...
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ISBN:
(纸本)0769509967
clustering is one of the important techniques in data mining. The objective of clustering is to group objects into clusters such that objects within a cluster are more similar to each other than objects in different clusters. The similarity between two objects is defined by a distance function, e.g., the Euclidean distance, which satisfies the triangular inequality. Distance calculation is computationally very expensive and many algorithms have been proposed so far to solve this problem. This paper considers the gradual clustering problem. From practice, we noticed that the user often begins clustering on a small number of attributes, e.g., two. If the result is partially satisfying the user will continue clustering on a higher number of attributes, e.g., ten. We refer to this problem as the gradual clustering problem. In fact gradual clustering can be considered as vertically incremental clustering. Approaches are proposed to solve this problem. The main idea is to reduce the number of distance calculations by using the triangle inequality. Our method first stores in an index the distances between a representative object and objects in n-dimensional space. Then these pre-computed distances are used to avoid distance calculations in (n+m)-dimensional space. Two experiments on real data sets demonstrate the added value of our approaches. The implemented algorithms are based on the DBSCAN algorithm with an associated M-Tree as index tree. However the principles of our idea can well be integrated with other tree structures such as MVP-Tree, R*-Tree, etc., and with other clustering algorithms.
In this paper, the design and implementation for computing the Single Linkage (SLINK) and Complete Linkage (CLINK) clustering algorithms on a Field Programmable Gate Array (FPGA) are presented. This research seeks to ...
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In this paper, the design and implementation for computing the Single Linkage (SLINK) and Complete Linkage (CLINK) clustering algorithms on a Field Programmable Gate Array (FPGA) are presented. This research seeks to extend the authors' previous work, in which novel systolic arrays for implementing the SLINK hierarchical clustering algorithm were presented. As is well known, hierarchical clustering algorithms find applications in many engineering areas, including pattern classification and image processing. However, the execution of these algorithms require considerable CPU time which makes them unsuitable for real-time applications. Our motivation for performing this research is, and has been since our earlier publications, to reduce the CPU time taken for executing the algorithms under discussion. In the present work, our focus is on the implementation of the proposed systolic arrays on a reconfigurable architecture, namely, a Xilinx FPGA. The reconfigurability aspect of the FPGA permits the implementation of both the SLINK and CLINK algorithms on the same FPGA.
作者：
Jorge M. SantosFrederico MoraisINEB
Biomedical Engineering Institute ISEP School of Engineering Polytechnic of Porto ISEP
School of Engineering Polytechnic of Porto
clustering algorithms are being widely used on biomedical data. They aim to extract important information that can be used to improve life conditions by helping specialized technicians on the decision process. Cluster...
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ISBN:
(纸本)9781479926060
clustering algorithms are being widely used on biomedical data. They aim to extract important information that can be used to improve life conditions by helping specialized technicians on the decision process. clustering algorithms based on information theory concepts claim that by using higher order statistic they are able to extract more information from the data and therefore provide much better results. In this work we try to verify this claim by comparing the performance of some entropic clustering algorithms against more conventional ones. Results of the performed experiments are not conclusive but they seem to indicate that this kind of entropic algorithms may provide some improvements when clustering biomedical data.
The paper identifies the scope of improvement for the search result of a web site. The study includes some commonly used clustering algorithms to identify the usage of clustering approach for improving web elements an...
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The paper identifies the scope of improvement for the search result of a web site. The study includes some commonly used clustering algorithms to identify the usage of clustering approach for improving web elements analysis, in various ways. As the Search result option is extensively used at almost every web site, the main focus is to optimize search result of a web site using clustering approach. Sementic web using the concept of ontology is included, to retrieve more relevant and meaning full serach results. Some most commomly used algorithms are experimented using web data, and it is observed that K-Means clustering algorithm gives best result in term of accuracy and speed. Thus the proposed hybrid model will be using K-Means and Genetic algorithm to overcome the drawbacks of K-Means. The evaluation parameters; accuracy in terms of objects placement in correct cluster, relevancy, speed and user satisfaction are the main parameters considered for the study.
The authors present modifications to Kohonen autoassociative maps to increase their efficiency for clustering and decrease their sensitivity to initial conditions. A new update rule is described for the classification...
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The authors present modifications to Kohonen autoassociative maps to increase their efficiency for clustering and decrease their sensitivity to initial conditions. A new update rule is described for the classification for similarity. Some test results are presented for comparison between different algorithms. The new neural network algorithm was applied to the problem of preplacement of VLSI cells with improvement in the quality of the solution and computational time.< >
Results from any existing clustering algorithm that are used for segmentation are highly sensitive to features that limit their generalization. Shape is one important attribute of an object. The detection and separati...
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Results from any existing clustering algorithm that are used for segmentation are highly sensitive to features that limit their generalization. Shape is one important attribute of an object. The detection and separation of an object using fuzzy ring-shaped clustering (FKR) and elliptic ring-shaped clustering (FKE) already exists in the literature. Not all real objects however, are ring or elliptical in shape, so to address these issues, this paper introduces a new shape-based algorithm, called fuzzy image segmentation combining ring and elliptic shaped clustering algorithms (FCRE) by merging the initial segmented results produced by FKR and FKE. The distribution of unclassified pixels is performed by connectedness and fuzzy c-means (FCM) using a combination of pixel intensity and normalized pixel location. Both qualitative and quantitative analysis of the results for different varieties of images proves the superiority of the proposed FCRE algorithm compared with both FKR and FKE.
In the last few years, a number of available screening compounds has been growing rapidly due to the recent developments of high-throughput screening in drug discovery. Chemical vendors provide millions of compounds f...
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In the last few years, a number of available screening compounds has been growing rapidly due to the recent developments of high-throughput screening in drug discovery. Chemical vendors provide millions of compounds for drug lead identification; however, these compounds are highly redundant. clustering method that groups similar compounds into families, can be used to analyze such redundancy. One of most used clustering method is cluster-based compound selection, which involves subdividing a set of compounds into clusters and choosing one compound or a small number of compounds from each cluster. However, little research has been done on overlapping method fuzzy c-means (FCM) and fuzzy c-varieties (FCV) clustering algorithms in compound selection research. Therefore, these two clustering algorithms are implemented and the performance is analyzed based on the effectiveness of the clustering results in terms of mean intercluster molecular dissimilarity (MIMDS) where these results are compared with one another. The analysis shows that in terms of MIMDS, the FCV is better than FCM because it clearly shown the uniform results compare to FCM clustering algorithm.
This paper presents the results of some partitional clustering algorithms applied to the segmentation of color images in the RGB space. As more information is involved in the algorithm, and the distance measure is mor...
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This paper presents the results of some partitional clustering algorithms applied to the segmentation of color images in the RGB space. As more information is involved in the algorithm, and the distance measure is more flexible, the better the results. The selected algorithms for this work are the K-means, the FCM, the GK-B, and the GKPFCM. The GKPFCM gives the better results when all the algorithms are applied to the segmentation of two images, an image of bananas and the other one of tomates at different stages of ripeness in both cases. The results are interesting as it is possible to identify the objects, to determine the degree of ripeness, and to estimate the amount and proportion of ripe objects for a possible decision-making.
clustering is an unsupervised technique of Data Mining. It means grouping similar objects together and separating the dissimilar ones. Each object in the data set is assigned a class label in the clustering process us...
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clustering is an unsupervised technique of Data Mining. It means grouping similar objects together and separating the dissimilar ones. Each object in the data set is assigned a class label in the clustering process using a distance measure. This paper has captured the problems that are faced in real when clustering algorithms are implemented .It also considers the most extensively used tools which are readily available and support functions which ease the programming. Once algorithms have been implemented, they also need to be tested for its validity. There exist several validation indexes for testing the performance and accuracy which have also been discussed here.
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