Image segmentation is one of the pre-processing steps required to analyze color images. Image segmentation in RGB space, which is performed by using clustering algorithm, is required long computation time even in smal...
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Image segmentation is one of the pre-processing steps required to analyze color images. Image segmentation in RGB space, which is performed by using clustering algorithm, is required long computation time even in small images. Another approach that can be used for image segmentation is the histogram-based approach. However, histogram-based approaches can also be applied to single-channel or gray-scale images. Therefore, a hue-based approach is considered for segmentation in a color image. However, since the hue shows an angular change, it is not possible to use number line based operations. In this study, directional based clustering algorithms are used to solve this problem. The performance of directional based algorithms was measured and compared.
This article uses analytical methods to assess reductions in total costs of telematic systems that can result from common infrastructure utilization. Analytical methods based on clustering and K-minimum spanning tree ...
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This article uses analytical methods to assess reductions in total costs of telematic systems that can result from common infrastructure utilization. Analytical methods based on clustering and K-minimum spanning tree can be adopted for finding clusters or sets which maximize reductions in total system costs due to infrastructure sharing between telematic systems. Efficient integration of telematic systems through infrastructure sharing can positively influence telematic service interoperability while reducing costs. Results show the measure of synergy for each K-value, as well as total cost savings of up to 2%.
clustering is essentially a procedure of grouping a set of objects in such a manner that items within the same clusters are more akin to each other compared with those data point or objects in different amassments or ...
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ISBN:
(纸本)9781509062195
clustering is essentially a procedure of grouping a set of objects in such a manner that items within the same clusters are more akin to each other compared with those data point or objects in different amassments or clusters. This paper discusses partition-predicated clustering techniques, such as K-Means, K-Means++ and object predicated Fuzzy C-Means clustering algorithm. This paper proposes a method for getting better clustering results by application of sorted and unsorted data into the algorithms. Elapsed time & total number of iterations are the factors on which, the behavioral patterns are analyzed. The experimental results shows that passing the sorted data instead of unsorted data not only effects the time complexity but withal ameliorates performance of these clustering techniques.
Experiments are carried out on datasets with different dimensions selected from UCI datasets by using two classical clustering algorithms. The results of the experiments indicate that when the dimensionality of the re...
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Experiments are carried out on datasets with different dimensions selected from UCI datasets by using two classical clustering algorithms. The results of the experiments indicate that when the dimensionality of the real dataset is less than or equal to 30, the clustering algorithms based on distance are effective. For high-dimensional datasets--dimensionality is greater than 30, the clustering algorithms are of weaknesses, even if we use dimension reduction methods, such as Principal Component Analysis (PCA).
Classifying an unknown object in image retrieval systems using the nearest neighbour classifier would be very time consuming when the number of the objects within the associated database is high. Generating a dendrogr...
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Classifying an unknown object in image retrieval systems using the nearest neighbour classifier would be very time consuming when the number of the objects within the associated database is high. Generating a dendrogram using a Hierarchical Agglomerative clustering (HAC) algorithm and searching the database images from coarse to fine resolutions using image pyramids are two important groups of techniques widely used for dealing with this problem. In this paper, a novel algorithm is proposed by combining these methods within the framework of a face recognition system. The search process is performed in a coarse-to-fine manner using image pyramids. On the bottom level of the pyramid (the finest resolution), a set of dendrograms is formed using the HAC algorithm. Our experimental studies show that the recognition process can be speeded up by a factor of around 65 compare to the basic nearest neighbour classifier. In such a condition, however, the recognition rate is slightly reduced.
This paper uses clustering algorithms to introduce a shape framework for deformable objects. Until now, the shape detection of the deformable objects has faced several challenges: 1) unable to form a unified framework...
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One of the mechanisms used to enlarge the lifetime of wireless sensor networks (WSN) and to provide more efficient functioning procedures is clustering. By assuming roles within a cluster hierarchy, the nodes in a WSN...
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One of the mechanisms used to enlarge the lifetime of wireless sensor networks (WSN) and to provide more efficient functioning procedures is clustering. By assuming roles within a cluster hierarchy, the nodes in a WSN can control the activities they performed and therefore, reduce their energy consumption. However, the election of when to act as a data provider (saving energy) and when to act as a gateway (cluster head) between the nodes and the base station is not a simple task. To make this decision it is necessary to take into account aspects like power level signal, transmission schedules and networking functioning (proactive or reactive). In this paper we study some basic concepts related to the clustering process in WSN and presenting a comparison survey between different clustering protocols
Most of the existing Data Mining algorithms have been manually produced, that is, have been developed by a human programmer. A prominent Artificial Intelligence research area is automatic programming - the generation ...
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Most of the existing Data Mining algorithms have been manually produced, that is, have been developed by a human programmer. A prominent Artificial Intelligence research area is automatic programming - the generation of a computer program by another computer program. clustering is an important data mining task with many useful real-world applications. Particularly, the class of clustering algorithms based on the idea of data density to identify clusters has many advantages, such as the ability to identify arbitrary-shape clusters. We propose the use of Estimation of Distribution algorithms for the artificial generation of density-based clustering algorithms. In order to guarantee the generation of valid algorithms, a directed acyclic graph (DAG) was defined where each node represents a procedure (building block) and each edge represents a possible execution sequence between two nodes. The Building Blocks DAG specifies the alphabet of the EDA, that is, any possibly generated algorithm. Preliminary experimental results compare the clustering algorithms artificially generated by Autoclustering to DBSCAN, a well-known manually-designed algorithm.
clustering is the process of grouping related instances of unlabelled data into distinct subsets called clusters. While there are many different clustering methods available, almost all of them use simple distance-bas...
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clustering is the process of grouping related instances of unlabelled data into distinct subsets called clusters. While there are many different clustering methods available, almost all of them use simple distance-based (dis)similarity functions such as Euclidean Distance. However, these and most other predefined dissimilarity functions can be rather inflexible by considering each feature equally and not properly capturing feature interactions in the data. Genetic Programming is an evolutionary computation approach that evolves programs in an iterative process that naturally lends itself to the evolution of functions. This paper introduces a novel framework to automatically evolve dissimilarity measures for a provided clustering dataset and algorithm. The results show that the evolved functions create clusters exhibiting high measures of cluster quality.
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