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Velcin, Julien
Universiti Lumihre Lyon 2
Lyon, France
 
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- 10 of 18 reviews

   
  A novel probabilistic clustering model for heterogeneous networks
Deng Z., Xu X. Machine Learning 104(1): 1-24, 2016.  Type: Article

Following up works tagged as link mining, in which we fully consider the links between objects in the data mining process [1], this paper addresses the clustering task when performed on heterogeneous networks....

Aug 31 2016  
  Subspace clustering of data streams: new algorithms and effective evaluation measures
Hassani M., Kim Y., Choi S., Seidl T. Journal of Intelligent Information Systems 45(3): 319-335, 2015.  Type: Article

Recently, much attention has been paid to data that evolve over time, which are usually called data streams. This paper proposes a contribution for comparing the efficiency of different existing subspace clustering algorithms, meaning ...

Jun 7 2016  
  Pattern classification and clustering: a review of partially supervised learning approaches
Schwenker F., Trentin E. Pattern Recognition Letters 374-14, 2014.  Type: Article

Building efficient classifiers for supervised machine learning is a must for many applications, ranging from automatic recommendation to network intrusion detection. Similarly, clusters developed in an unsupervised learning framework a...

Apr 28 2015  
  MGR: an information theory based hierarchical divisive clustering algorithm for categorical data
Qin H., Ma X., Herawan T., Zain J. Knowledge-Based Systems 67401-411, 2014.  Type: Article

Most of the clustering literature deals with numeric data. This paper exposes a novel algorithm for clustering categorical data by following an “old school” top-down procedure. The main idea is very similar to clust...

Nov 18 2014  
  Absolute and relative clustering
Kamishima T., Akaho S.  MultiClust 2013 (Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering, Chicago, IL, Aug 11-14, 2013) 1-6, 2013.  Type: Proceedings

One usually argues that clustering is an ill-posed problem [1]. The reason may be that there are several ways of addressing this issue. In constraint clustering, the way in which knowledge is integrated will result in transductive clus...

Sep 15 2014  
  The impact of semi-supervised clustering on text classification
Kyriakopoulou A., Kalamboukis T.  PCI 2013 (Proceedings of the 17th Panhellenic Conference on Informatics, Thessaloniki, Greece, Sep 19-21, 2013) 180-187, 2013.  Type: Proceedings

Categorizing texts into predefined classes is an important issue relevant to many fields, including data mining, natural language processing, and machine learning. In this paper, the authors build on previous work that investigates how...

Apr 21 2014  
  Vague C-means clustering algorithm
Xu C., Zhang P., Li B., Wu D., Fan H. Pattern Recognition Letters 34(5): 505-510, 2013.  Type: Article

Clustering data into fuzzy or vague categories is an essential task for data mining and information retrieval applications. In this paper, the authors propose adapting the classical fuzzy C-means (FCM) algorithm to vague sets, an exten...

Jun 20 2013  
  A new method of mining data streams using harmony search
Karimi Z., Abolhassani H., Beigy H. Journal of Intelligent Information Systems 39(2): 491-511, 2012.  Type: Article

Supervised classification is the most common task in machine learning. It is included in many applications, including spam detection, face recognition, and sentiment analysis. This paper investigates the potential application of harmon...

Mar 26 2013  
  EventRiver: visually exploring text collections with temporal references
Luo D., Yang J., Krstajic M., Ribarsky W., Keim D. IEEE Transactions on Visualization and Computer Graphics 18(1): 93-105, 2012.  Type: Article

Searching and tracking events in news data is becoming a crucial issue for data analysts. This paper builds on a seminal work sponsored by DARPA in the 1990s. The objective is to provide a graphical tool, EventRiver, that allows the us...

Feb 21 2013  
  A new algorithm for initial cluster centers in k-means algorithm
Erisoglu M., Calis N., Sakallioglu S. Pattern Recognition Letters 32(14): 1701-1705, 2011.  Type: Article

In unsupervised machine learning, algorithms based on the classical k-means framework provide local optimal solutions. This is why it is really important to begin the optimization process with a good initial partitio...

Dec 28 2011  
 
 
 
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