In data mining two important tasks involved are classification and clustering. In general, in classification the classifier assigns a class label from a set of predefined classes to a new input object. Whereas, given a set of objects, clustering creates different groups of these objects using some similarity measure. In the context of machine learning, classification is supervised learning and ...

In data mining two important tasks involved are classification and clustering. In general, in classification the classifier assigns a class label from a set of predefined classes to a new input object. Whereas, given a set of objects, clustering creates different groups of these objects using some similarity measure. In the context of machine learning, classification is supervised learning and ...

Robust eigenstructure clustering by non-smooth optimisation Minh Ngoc Dao, Dominikus Noll & Pierre Apkarian a Institut de Mathématiques, Université de Toulouse, Toulouse, France b Department of Mathematics and Informatics, Hanoi National University of Education, Hanoi, Vietnam c Control System Department, ONERA, Toulouse, France Accepted author version posted online: 19 Jan 2015.Published onlin...

We propose a traffic congestion estimation system based on unsupervised on-line learning algorithm. The system does not rely on background extraction or motion detection. It extracts local features inside detection regions of variable size which are drawn on lanes in advance. The extracted features are then clustered into two classes using K-means and Gaussian Mixture Models(GMM). A Bayes class...

Data warehouses (DW) and OLAP systems are business intelligence technologies allowing the on-line analysis of huge volume of data according to users’ needs. The success of DW projects essentially depends on the design phase where functional requirements meet data sources (mixed design methodology) (Phipps and Davis, 2002). However, when dealing with complex applications existing design methodol...

This paper describes the k-means range algorithm, a combination of the partitional k-means clustering algorithm with a well known spatial data structure, namely the range tree, which allows fast range searches. It offers a real-time solution for the development of distributed interactive decision aids in e-commerce since it allows the consumer to model his preferences along multiple dimensions,...

Estimating the elbow angle using shoulder data is very important and valuable in Functional Electrical Stimulation (FES) systems which can be useful in assisting C5/C6 SCI patients. Much research has been conducted based on the elbow-shoulder synergies.The aim of this study was the online estimation of elbow flexion/extension angle from the upper arm acceleration signals during ADLs. For this, ...

MOTIVATION Clustering of chemical and biochemical data based on observed features is a central cognitive step in the analysis of chemical substances, in particular in combinatorial chemistry, or of complex biochemical reaction networks. Often, for reasons unknown to the researcher, this step produces disappointing results. Once the sources of the problem are known, improved clustering methods m...

Clustering techniques have more importance in data mining especially when the data size is very large. It is widely used in the fields including pattern recognition system, machine learning algorithms, analysis of images, information retrieval and bio-informatics. Different clustering algorithms are available such as Expectation Maximization (EM), Cobweb, FarthestFirst, OPTICS, SimpleKMeans etc...

the arc costs are assumed to be online parameters of the network and decisions should be made while the costs of arcs are not known. the policies determine the permitted nodes and arcs to traverse and they are generally defined according to the departure nodes of the current policy nodes. in on-line created tours arc costs are not available for decision makers. the on-line traversed nodes are f...