An Approach of Concept Lattice Theory in Data Mining and Its Applications
Concept lattice has typically been shown to be a very powerful method and design for data mining. Depending on the type of data, it is commonly used for data analysis and information discovery and various concept lattice-based methods are used. The aim of this extended version paper is to present one application of the theory of lattice in text mining and another in image mining. The principle of lattice theory was applied in the first approach by using one of its components often used in data mining, the formal concept analysis that has a strong procedure, the extraction of the association rule that helps to identify patterns that often appear together in a database. The use of the lattice principle for characterizing image sets was seen in the second one by using landmarks to allow a computer to automatically identify objects with respect to the image class to which they belong. For text mining, the discovery of association rules mostly uses formal concept analysis to analyze the relationships between patterns that simultaneously appear.
Author (s) Details
Pascal Sungu Ngoy
Université Nouveaux Horizons (UNH), DR Congo.
Prof. Kaninda Musumbu
Université Nouveaux Horizons (UNH), DR Congo and Université de Bordeaux, France.
African Institute for Mathematical Sciences (AIMS), Cameroon.
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