Algorithms for Building Concept Sets and Concept Lattices (original) (raw)
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Concept lattice has been proven to be a very e ective tool and architecture for data mining in general. It is widely used for data analysis and knowledge discovery and various concept lattice based approaches are used depending on the type of data. This paper aims at presenting one application of the lattice theory : the text mining. In this approach, we applied the notion of lattice theory by using one of its components mostly used in data mining, the formal concept analysis which has a powerful method, the association rule extraction which helps to nd in a database patterns which appear frequently together.
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Journal of Science and Technology: Issue on Information and Communications Technology
Since the 1980s, the concept lattice was studied and applied to the problems of text mining, frequent itemset, classification, etc. The formal concept analysis - FCA is one of the main techniques applied in the concept lattice. FCA is a mathematical theory which is applied to the data mining by setting a table with rows describing objects and columns describing attributes, with relationships between them, and then sets up the concept lattice structure. In the area of information retrieval, FCA considers the correlation of objects-attributes the same as those of documents-terms. In the process of setting up the lattice, FCA defines each node in the lattice as a concept. The algorithm for the construction of concept lattice will install a couple on each node, including a set of documents with common terms, and a set of terms which co-occurs in documents. In a larger scale, each concept in the lattice could be recognized as a couple of questions - answers. In the lattice, the action of...