Computational Intelligence (CI) consists of an evolving collection of methodologies often inspired from nature (Bonissone, Chen, Goebel & Khedkar, 1999, Fogel, 1999, Pedrycz, 1998). Two popular methodologies of CI include neural networks and fuzzy systems. Lately, a unification was proposed in CI...
A Review on Data Clustering Algorithms for Mixed Data Clustering is the unsupervised classification of patterns into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of ... DH...
Traf?c Classi?cation Using Clustering Algorithms Jeffrey Erman, Martin Arlitt, Anirban Mahanti University of Calgary, 2500 University Drive NW, Calgary, AB, Canada {erman, arlitt, mahanti}@cpsc.ucalgary.ca ABSTRACT Classi?cation of network traf?c using port-based or payload-based analysis is ...
As the main work of this paper, firstly the complex high-dimensional original sample data are reduced dimensionally, and then the cluster analysis is performed. By comparing the results of several commonly used clustering algorithms, the optimal algorithm is used to classify the user power load ...
The effectiveness of the proposed FRSAC algorithm, along with a comparison with existing supervised and unsupervised gene selection and clustering algorithms, is demonstrated on six cancer and two arthritis data sets based on the class separability index and predictive accuracy of the naive Bayes' ...
(Furby et al., 2008; Wulder et al., 2008a). Despite the attractiveness of the automatic character of clustering algorithms, they become time consuming when the data dimension is high or the data volume large (Chen and Gong, 2013), and interpreting clusters properly is a challenging and ...
Noisy data, or the presence of outliers, can significantly degrade the performance of these algorithms. Therefore, with noisy datasets, caused by images with different types of lighting, non-clustering algorithms may be preferred; however, Keke et al.5 implemented an improved version of the fuzzy...
toremoveconfusionamongdifferentclustersandbuildabridgebetweenlinearandnonlinearmanifoldclusteringalgorithms.OuranalysisrevealsthatK-flatssuffersfromthreekindsofdeterioration,i.e.,in-trinsicerrors,infinityerrorsandco-linearerrors,whicharemainlyrootedintherecon-structionerrormeasureandtheinfinitelyextendingrepresentationsof...
The overall classification success rate, calculated over the entire set of points, is a measure of the degree of clustering in the set of data. Clearly, a majority vote of the KNNs can only occur if the majority of the measurement variables concur, because the data is usually autoscaled. ...
The experimental results show that our algorithm greatly improves the classification performance over the traditional learning algorithms. 展开 关键词: Kullback-Leibler divergence classification co-clustering out-of-domain DOI: 10.1145/1281192.1281218 被引量: 669 ...