Data mining or knowledge discovery in database (KDD) refers to the non-trivial process of discovering interesting, implicit, and previously unknown knowledge from databases. Such a task implies to be able to perform analyses both on high-dimensional input data and large dataset. The most popular models used in KDD are the symbolic models. Unfortunately, these models suffer of very serious limitations. Rule generation is a highly time-consuming process that generates a huge number of rules, including a large ratio of redundant rules. Hence, this prohibits any kind of rule computation and selection as soon as data are numerous and they are represented by very high-dimensional description space. This latter situation is very often encountered with documentary data. To cope with these problems, preliminary KDD trials using numerical models have been made. An algorithm for knowledge extraction from self-organizing network has been proposed in [8]. This approach is based on a supervised generalized relevance learning vector quantization (GRLVQ) which is used for extracting decision trees. The different paths of the generated trees are then used for denoting rules. Nevertheless, the main defect of this method is to necessitate training data. On our own side, we have proposed a hybrid classification method for mapping an explicative structure issued from a symbolic classification into an unsupervised numerical self-organizing map (SOM) [15]. SOM map and Galois lattice are generated on the same data. The cosine projection is then used for associating lattice concepts to the SOM classes. Concepts properties act as explanation for the SOM classes. Furthermore, lattice pruning combined with migration of the associated SOM classes towards the top of the pruned lattice is used to generate explanation of increasing scope on the SOM map. Association rules can also be produced in such a way. Although it establishes interesting links between numerical and symbolic worlds this approach necessitates the time-consuming computation of a whole Galois lattice. In a parallel way, in order to enhance both the quality and the granularity of the data analysis and to reduce the noise which is inevitably generated in an overall classification approach, we have introduced the multi-viewpoint analysis and multilevel clustering approach based on a significant extension of the SOM model, named MultiSOM [19][25]. The viewpoint building principle consists in separating the description of the data into several sub-descriptions corresponding different property subsets or even different data subsets. In MultiSOM each viewpoint is represented by a single SOM map.
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A New Multi-Viewpoint and Multi-Level Clustering Paradigm for Efficient Data Mining Tasks
Semantic Scholar · Computer Science · 2011
Abstract
Data mining or knowledge discovery in database (KDD) refers to the non-trivial process of discovering interesting, implicit, and previously unknown knowledge from databases. Such a task implies to be able to perform analyses both on high-dimensional input data and large dataset. The most popular models used in KDD are the symbolic models. Unfortunately, these models suffer of very serious limitations. Rule generation is a highly time-consuming process that generates a huge number of rules, including a large ratio of redundant rules. Hence, this prohibits any kind of rule computation and selection as soon as data are numerous and they are represented by very high-dimensional description space. This latter situation is very often encountered with documentary data. To cope with these problems, preliminary KDD trials using numerical models have been made. An algorithm for knowledge extraction from self-organizing network has been proposed in [8]. This approach is based on a supervised generalized relevance learning vector quantization (GRLVQ) which is used for extracting decision trees. The different paths of the generated trees are then used for denoting rules. Nevertheless, the main defect of this method is to necessitate training data. On our own side, we have proposed a hybrid classification method for mapping an explicative structure issued from a symbolic classification into an unsupervised numerical self-organizing map (SOM) [15]. SOM map and Galois lattice are generated on the same data. The cosine projection is then used for associating lattice concepts to the SOM classes. Concepts properties act as explanation for the SOM classes. Furthermore, lattice pruning combined with migration of the associated SOM classes towards the top of the pruned lattice is used to generate explanation of increasing scope on the SOM map. Association rules can also be produced in such a way. Although it establishes interesting links between numerical and symbolic worlds this approach necessitates the time-consuming computation of a whole Galois lattice. In a parallel way, in order to enhance both the quality and the granularity of the data analysis and to reduce the noise which is inevitably generated in an overall classification approach, we have introduced the multi-viewpoint analysis and multilevel clustering approach based on a significant extension of the SOM model, named MultiSOM [19][25]. The viewpoint building principle consists in separating the description of the data into several sub-descriptions corresponding different property subsets or even different data subsets. In MultiSOM each viewpoint is represented by a single SOM map.