A new data topology matching technique with Multilevel Interior Growing Self-Organizing Maps

Ayadi, Thouraya; Hamdani, Tarek M.; Alimi, Adel M.

doi:10.1109/icsmc.2010.5641936

Cited by 9 publications

(10 citation statements)

References 16 publications

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“…In fact, we remarked that the GT structure used in other algorithms such as MIGSOM becomes complicated and takes big values as the training process grows (Ayadi et al 2010;Alimi 2000Alimi , 2003Alimi et al 2003). In addition, when the dataset is large the computing process becomes more complicated at next iterations and takes more time with the augmentation of GT values.…”

Section: (A) (B)mentioning

confidence: 96%

“…Our approach is based on the MIGSOM algorithm (Ayadi et al 2010 which adapts its structure according to the data form. The fuzzy aspect of our method is inspired from the FKCN algorithm (Pascual et al 2000).…”

Section: Fmig: Fuzzy Multilevel Interior Gsomsmentioning

confidence: 99%

“…In fact, there is no sharp boundary between classes so that fuzzy clustering is better suited for the data classification. Therefore, to classify such data is a very challenging task as in Ayadi et al (2007Ayadi et al ( , 2010Ayadi et al ( , 2011, Alimi (2000Alimi ( , 2003, Hamdani et al (2011a), Dhahri and Alimi (2005), Kohonen (1998), Denaïa et al (2007) and Chang and Liao (2006). The clustering is an unsupervised classification mechanism where a set of entities are classified into a number of homogeneous clusters, with respect to a given similarity measure.…”

Section: Introductionmentioning

confidence: 98%

“…The clustering is an unsupervised classification mechanism where a set of entities are classified into a number of homogeneous clusters, with respect to a given similarity measure. Due to the fuzzy nature of many classification problems, a number of fuzzy clustering methods have been developed such as in Ayadi et al (2007Ayadi et al ( , 2010Ayadi et al ( , 2011, Alimi (2003), Hamdani et al (2008Hamdani et al ( , 2011a, El Malek et al (2002), Pascual et al (2000), Kohonen (1998Kohonen ( , 2001, Denaïa et al (2007), Fritzke (1994), Petterssona et al (2007) , Abraham and Nath (2001), Aguilera and Frenich (2001) and Chang and Liao (2006). Fuzzy clustering can be applied as an unsupervised learning technique to classify patterns (Alimi 2000(Alimi , 2003Alimi et al 2003).…”

Section: Introductionmentioning

confidence: 98%

“…This algorithm is a hybrid approach in which both the MIGSOM technique (Multilevel Interior Growing Self-Organizing Maps) (Ayadi et al 2010 and the FKCN algorithm (Fuzzy Kohonen Clustering Network) (Pascual et al 2000) have been integrated. Our approach introduces a new form of fuzzy growing process and attempts to overcome some of the clustering difficulties by taking advantage of the best features of the multilevel structure given by MIGSOM and the fuzzy clustering model presented by FKCN.…”

Section: Introductionmentioning

confidence: 99%

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Performance evaluation of FMIG clustering using fuzzy validity indexes

et al. 2014

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The clustering of high-dimensional data presents a critical computational problem. Therefore, it is convenient to arrange the cluster centres on a grid with a small dimensional space that reduces computational cost and can be easily visualized. Moreover, in real applications there is no sharp boundary between classes, real datasets are naturally defined in a fuzzy context. Therefore, fuzzy clustering fits better for complex real datasets to determine the best distribution. Selforganizing map (SOM) technique is appropriate for clustering and vector quantization of high-dimensional data. In this paper we present a new fuzzy learning approach called FMIG (fuzzy multilevel interior growing self-organizing maps). The proposed algorithm is a fuzzy version of MIGSOM (multilevel interior growing self-organizing maps). The main contribution of FMIG is to define a fuzzy process of data mapping and to take into account the fuzzy aspect of high-dimensional real datasets. This new algorithm is able to auto-organize the map accordingly to the fuzzy training property of the nodes. In the second step, the introduced scheme for FMIG is clusCommunicated by E. Lughofer. tered by means of fuzzy C-means (FCM) to discover the interior class distribution of the learned data. The validation of FCM partitions is directed by applying six validity indexes. Superiority of the new method is demonstrated by comparing it with crisp MIGSOM, GSOM (growing SOM) and FKCN (fuzzy Kohonen clustering network) techniques. Thus, our new method shows improvement in term of quantization error, topology preservation and clustering ability.

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Section: (A) (B)mentioning

confidence: 96%

Section: Fmig: Fuzzy Multilevel Interior Gsomsmentioning

confidence: 99%