Abstract
When some attributes of a formal context can be decomposed into some subattributes a model of layered concept lattice to improve the efficiency of building concept lattice with complex structure attribute data is studied, the relationship between concept lattice and layered concept is discussed. Two algorithms are proposed: one is the roll-up building algorithm in which the upper concepts are built by the lower concept and the other is the drill-down algorithm in which the lower concepts are built by the upper concept. The examples and experiments show that the layered concept lattice model can be used to model complex structure attribute data, and the roll-up building algorithm and the drill-down algorithm are effective. The layered concept lattice model expands the scope of the research and application of concept lattice, the roll-up building algorithm, and drill-down algorithm of layered concept lattice to improve the efficiency for building concept lattice.
Highlights
Humans usually describe and recognize objective things from different levels and different granularity. ere is a process of deepening the attribute characteristics of objective things, and the knowledge concepts at different levels or at different granularity are obtained [1, 2]
Granular computing is a kind of useful mathematical method for processing complex structure data, and the idea of granular computing fits perfectly with the hierarchical and granular thinking mode of “from coarse to fine, from whole to part” in the process of human cognition [3,4,5]. e idea of granular computing originated from professor Zadeh [6]
Since Lin summarized relevant studies and introduced the term granular computing in 1998 [7], the thinking and methods of granular computing have appeared in many fields, such as rough set, fuzzy set, evidence theory, cluster analysis, machine learning, data mining, and knowledge discovery
Summary
Humans usually describe and recognize objective things from different levels and different granularity. ere is a process of deepening the attribute characteristics of objective things, and the knowledge concepts at different levels or at different granularity are obtained [1, 2]. An application often deals with both structured data and semistructured, unstructured data such as text, images, audio, video, video, and Web. In order to apply formal concept analysis to study knowledge representation and knowledge discovery in big data environment, the first thing to do is to extend the existing concept lattice model. In the process of extending the concept lattice model in big data environment, the attribute values used to describe the characteristics of unstructured data, such as text, images, audio, video, and Web, include word values, text values, vector values, and their composite values, in addition to the usual number value and character value [26, 27]. In order to satisfy the need for describing in detail the knowledge concept on unstructured dataset, the thinking of granular computing is applied to the formal concept analysis of data with complex structure. E roll-up building algorithm in which the upper concept is built by the lower concept and the drill-down algorithm in which the lower concept is built by the upper concept are proposed. e examples and experiments show that the rollup and the drill-down algorithms are effective
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