Computational morphological analysis comprises the development of measures (indicators) that describe different form attributes of a neuron and provides additional parameters for classification algorithms. Our work ad...
详细信息
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 ...
详细信息
ISBN:
(纸本)9781457721977
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%.
A tool to provide an idea of the content of a given video is becoming a need in the current Web scenario, where the presence of videos is increasing day after day. Dynamic summarization techniques can be used to this ...
详细信息
ISBN:
(纸本)9781424414567
A tool to provide an idea of the content of a given video is becoming a need in the current Web scenario, where the presence of videos is increasing day after day. Dynamic summarization techniques can be used to this aim as they set up a video abstract, by selecting and sequencing short video clips extracted from the original video. Needless to say, the selection process is critical. In this paper we focus our attention on clustering algorithms to provide such selection and we investigate the effects of their employment in the web scenario. clustering algorithms are very effecting in producing static video summary, but few works consider them for video abstract production. For this reason, we set up an experimental scenario where we investigate their performance considering different categories of video, different abstract lengths and different low-level video analysis. Results show that clustering techniques can be useful only for some categories of videos and only if the selection process is based on video scene characteristics. Furthermore, the investigation also shows that to provide a customized service (user can freely decide the abstract time length), only fast clustering algorithm should be used.
We present a novel framework that applies a meta-learning approach to clustering algorithms. Given a dataset, our meta-learning approach provides a ranking for the candidate algorithms that could be used with that dat...
详细信息
ISBN:
(纸本)9781424418206
We present a novel framework that applies a meta-learning approach to clustering algorithms. Given a dataset, our meta-learning approach provides a ranking for the candidate algorithms that could be used with that dataset. This ranking could, among other things, support non-expert users in the algorithm selection task. In order to evaluate the framework proposed, we implement a prototype that employs regression support vector machines as the meta-learner. Our case study is developed in the context of cancer gene expression microarray datasets.
Partitional algorithms form an extremely popular class of clustering algorithms. Primarily, these algorithms can be classified into two sub-categories: a) k-means based algorithms that presume the knowledge of a suita...
详细信息
ISBN:
(纸本)9783642172977
Partitional algorithms form an extremely popular class of clustering algorithms. Primarily, these algorithms can be classified into two sub-categories: a) k-means based algorithms that presume the knowledge of a suitable k, and b) algorithms such as Leader, which take a distance threshold value, tau, as an input. In this work, we make the following contributions. We 1) propose a novel technique, EPIC, which is based on both the number of clusters, k and the distance threshold, tau, 2) demonstrate that the proposed algorithm achieves better performance than the standard k-means algorithm, and 3) present a generic scheme for integrating EPIC into different classification algorithms to reduce their training time complexity.
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...
详细信息
ISBN:
(纸本)9781479926053;9781479926046
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.
Since little prior knowledge about remote sensing images can be obtained before performing recognition tasks, various unsupervised classification methods have been applied to solve such problem. Therefore, choosing an...
详细信息
ISBN:
(纸本)9783540877318
Since little prior knowledge about remote sensing images can be obtained before performing recognition tasks, various unsupervised classification methods have been applied to solve such problem. Therefore, choosing an appropriate clustering method is very critical to achieve good results. However, there is no standard criterion on which clustering method is more suitable or more effective. In this paper, we conduct a comparative study on three clustering methods, including C-Means, Finite Mixture Model clustering. and Affinity Propagation. The advantages and disadvantages of each method are evaluated by experiments and classification results.
Recommender Systems have been intensively used in Information Systems in the last decades, facilitating the choice of items individually for each user based on your historical. clustering techniques have been frequent...
详细信息
ISBN:
(纸本)9781733632546
Recommender Systems have been intensively used in Information Systems in the last decades, facilitating the choice of items individually for each user based on your historical. clustering techniques have been frequently used in commercial and scientific domains in data mining tasks and visualization tools. However, there is a lack of secondary studies in the literature that analyze the use of clustering algorithms in Recommender Systems and their behavior in different aspects. In this work, we present a Systematic Literature Review (SLR), which discusses the different types of information systems with the use of the clustering algorithm in Recommender Systems, which typically involves three main recommendation approaches found in literature: collaborative filtering, content-based filtering, and hybrid recommendation. In the end, we did a quantitative analysis using K-means clustering for finding patterns between clustering algorithms, recommendation approaches, and some datasets used in their publications.
The popular fuzzy c-means algorithm (FCM) converges to a local minimum of the objective function. Hence, different initializations may lead to different results. The important issue is how to avoid getting a bad local...
详细信息
ISBN:
(纸本)9780769535630
The popular fuzzy c-means algorithm (FCM) converges to a local minimum of the objective function. Hence, different initializations may lead to different results. The important issue is how to avoid getting a bad local minimum value to improve the cluster accuracy. The particle swarm optimization (PSO) is a popular and robust strategy for optimization problems. But the main difficulty in applying PSO to real-world applications is that PSO usually need a large number of fitness evaluations before a satisfying result can be obtained. In this paper, the improved new algorithm, "Fuzzy C-Mean based on Picard iteration and PSO (PPSO-FCM)", is proposed. Two real data sets were applied to prove that the performance of the PPSO-FCM algorithm is better than the conventional FCM algorithm and the PSO-FCM algorithm.
One of the aspects, of a clustering algorithm that should be considered for choosing an appropriate algorithm in an unsupervised learning task is stability. A clustering algorithm is stable (on a dataset) if it result...
详细信息
ISBN:
(纸本)9780769534404
One of the aspects, of a clustering algorithm that should be considered for choosing an appropriate algorithm in an unsupervised learning task is stability. A clustering algorithm is stable (on a dataset) if it results in the same clustering as it performed on the whole dataset, when actually performs on a (sub)sample of the dataset. In this paper, we report the results of an empirical study on the stability of two clustering algorithms, namely k-Means and normalized spectral clustering, along with some analysis on those results that are useful for practitioners who deal with scalability and researchers who employ stability as a tool for model selection.
暂无评论