The growing hierarchical self-organizing map

Dittenbach, Michael; Merkl, Dieter; Rauber, Andreas

doi:10.1109/ijcnn.2000.859366

Cited by 200 publications

(106 citation statements)

References 7 publications

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“…After that, we removed the most common stopwords, using an English stoplist, and accomplished a stemming phase with the algorithm by Porter (1980). After applying the TF-IDF term weighting function to find the significance of a feature (word) in tag representation (being the IDF factor log N d f (t) , where N is the collection size and d f (t) is the document frequency of a tag t), a dimensionality reduction stage was carried out to reduce the number of features per vector: we removed the terms with high (0.6) and low (0.02) document frequency values (Dittenbach et al (2000)). …”

Section: Representation By Document Contentmentioning

confidence: 99%

“…SOM has proven to be a effective way not only to organize information, but also to visualize it, and even to allow content addressable searches (Vesanto and Alhoniemi, 2000;Dittenbach et al, 2000;Russell et al, 2002;Perelomov et al, 2002;Roh et al, 2003;Jieh-Haur and Chen, 2012;Barrón-Adame et al, 2012). …”

Section: The Self-organizing Map (Som)mentioning

confidence: 99%

“…AQE measures the average distance between input vectors and their associated reference vectors in the map. Other issues about the SOM are the same as in the standard implementation SOMlib 4 Dittenbach et al (2000).…”

Section: The Self-organizing Map (Som)mentioning

confidence: 99%

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Reorganizing clouds: A study on tag clustering and evaluation

García-Plaza

Zubiaga

Fresno

et al. 2012

Expert Systems with Applications

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Finding and visualizing semantic relations among tags within a tag cloud enhances user experience, particularly regarding access to and retrieval of web pages on social tagging systems. Several approaches have been proposed to visualize tag relations in these systems. However, results of previous research rely on qualitative evaluation methods, and do not provide robust and sound comparison criteria. In order to allow quantitative evaluation we present a benchmark social tagging dataset, where a subset of 140 tags from a well-known social bookmarking site, Delicious, have been manually categorized according to the Open Directory Project (ODP). The manual categorization is utilized as a ground truth that enables quantitative evaluation providing a way of inferring the best of different clustering approaches. With this dataset we also explore different tag representation approaches to present a reorganized tag cloud by using Self Organizing Maps. In addition, we present an approach to enrich the resultant tag cloud with the most characteristic terms for each tag and group of tags, making possible a further filtered navigation, both by tag and document content, and easing a deeper qualitative evaluation of the clusters.

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Section: Representation By Document Contentmentioning

confidence: 99%

Section: The Self-organizing Map (Som)mentioning

confidence: 99%

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Reorganizing clouds: A study on tag clustering and evaluation

García-Plaza

Zubiaga

Fresno

et al. 2012

Expert Systems with Applications

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“…The GHSOM algorithm is based upon a structure composed of Self-Organizing Maps (SOM) that grow, by increasing the number of nonlinear units, until a quality criterion of the classification has been satisfied (Dittenbach, et al, 2000). The growing process builds a hierarchical structure composed by SOM in which lower layers classify data with more granularity, as shown in Fig.…”

Section: Growing Hierarchical Self Organizing Mapsmentioning

confidence: 99%

Nonlinear Strategy to Classify Time Series of the Semiconductor Market Trend

Bucolo

Caizzone

Fortuna

et al. 2005

IFAC Proceedings Volumes

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Semiconductor firm sales are a complex function of what direct customers require and how efficiently the company is able to satisfy them. The task of solving the management of production planning with a wide products portofolio is not an easy one. The paper deals with the adoption of an unsupervised clustering strategy to classify products, not just in relation with the business parameters, but also by considering the historical evolution of sales and customer demand. Different nonlinear techniques have been considered to face the problem both from the mathematical point of view than from the economic one. Particularly, this strategy has been implemented to classify the products of Discrete and Standard Ics Group of the worldwide semiconductor firm, STMicroelectronics in relation to their sales versus the different customer type. Two different approaches have been used: the hierarchical clustering with an optimization procedure and GHSOM structures. The results show the validity of the cluster strategy, in fact the time series carrying on the same qualitative information are grouped, and the flexibility of the GHSOM. Moreover, the GHSOM, as a clustering technique, requires a low supervision level and brings a reduction of the problem complexity and speeds up the process.Copyrigh©2005IFAC

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“…Some of the extensions of the SOM algorithm and architecture address the disadvantages of fixed size and missing hierarchical representation. One of the SOMbased models implementing an algorithm dealing with both issues is the Growing Hierarchical Self-Organizing Map (GH-SOM) [5], [6], [7]. The GHSOM is a neural network architecture combining the advantages of two principal extension of the Self-Organizing Map, dynamic growth and hierarchical structure.…”

Section: Introductionmentioning

confidence: 99%

Investigation of alternative strategies and quality measures for controlling the growth process of the growing hierarchical self-organizing map

Dittenbach¹,

Rauber²,

Pölzlbauer³

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

Self Cite

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Abstract-The Self-Organizing Map (SOM) is a very popular neural network model for data analysis and visualization of high-dimensional input data. The Growing Hierarchical SelfOrganizing Map (GHSOM) -being one of the many architectures based on the SOM -has the property of dynamically adapting its architecture during training by map growth as well as creating a hierarchical structure of maps, thus reflecting hierarchical relations in the data. This allows for viewing portions of the data at different levels of granularity. We review different SOM quality measures and also investigate alternative strategies as candidates for guiding the growth process of the GHSOM in order to improve the hierarchical representation of the data.

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The growing hierarchical self-organizing map

Cited by 200 publications

References 7 publications

Reorganizing clouds: A study on tag clustering and evaluation

Reorganizing clouds: A study on tag clustering and evaluation

Nonlinear Strategy to Classify Time Series of the Semiconductor Market Trend

Investigation of alternative strategies and quality measures for controlling the growth process of the growing hierarchical self-organizing map

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