Sequence-based SOM: Visualizing transition of dynamic clusters

We have proposed neural-network based visualization approach, called sequence-based SOM (self-organizing map) that visualizes transition of dynamic clusters by introducing the sequencing weight function onto the neuron topology. This approach mitigates the problems with a sliding window-based method. In this paper, we confirmed the properties of the proposed method via artificial data sets, and a real news articles data set by showing the topicspsila derivation and diversification/convergence. Visualization of cluster transition aids in the comprehension of such phenomena which come useful in various domains such as fault diagnosis and medical check-up, among others.

[1]  Masayuki Numao,et al.  Visualizing Dynamics of the Hot Topics Using Sequence-Based Self-organizing Maps , 2005, KES.

[2]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[3]  James Allan,et al.  Topic detection and tracking: event-based information organization , 2002 .

[4]  Masayuki Numao,et al.  Visualization Architecture Based on SOM for Two-Class Sequential Data , 2006, KES.

[5]  Vangelis Karkaletsis,et al.  Enriching OWL Ontologies with Linguistic and User-Related Annotations: The ELEON System , 2007 .

[6]  M. Kimura,et al.  Multinomial PCA for extracting major latent topics from document streams , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[7]  Sadaaki Miyamoto,et al.  LVQ Clustering and SOM Using a Kernel Function , 2005 .

[8]  John W. Sammon,et al.  A Nonlinear Mapping for Data Structure Analysis , 1969, IEEE Transactions on Computers.

[9]  Yoshiharu Ishikawa,et al.  T-Scroll: Visualizing Trends in a Time-Series of Documents for Interactive User Exploration , 2007, ECDL.

[10]  David Jensen,et al.  TimeMines: Constructing Timelines with Statistical Models of Word Usage , 2000, KDD 2000.

[11]  Graham K. Rand,et al.  Quantitative Applications in the Social Sciences , 1983 .

[12]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[13]  D. M. Hutton,et al.  Web Dynamics - Adapting to Change in Content, Size, Topology and Use , 2006 .

[14]  Masayuki Numao,et al.  Combining Burst Extraction Method and Sequence-Based SOM for Evaluation of Fracture Dynamics in Solid Oxide Fuel Cell , 2007 .

[15]  Mark Levene,et al.  Web dynamics : adapting to change in content, size, topology and use , 2004 .