Knowledge discovery in data using formal concept analysis and random projections

Cherukuri, Aswani Kumar

doi:10.2478/v10006-011-0059-1

Cited by 53 publications

(18 citation statements)

References 27 publications

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Mentioning

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“…12,13,14,15,16,17,18 C C C C C C C and 19 C are the origin concepts by examining the formal context in Table 3. The least concept 20 C does not possess the corresponding meanings in the geospatial domain, due to the completeness to form the bottom of the concept lattice.…”

Section: Case Study and Discussionmentioning

confidence: 99%

“…FCA, a branch of applied mathematics based on lattice theory, is a conceptual framework proposed by Wille in 1982, and has been applied to many different fields, such as data analysis, knowledge discovery, software engineering, and information retrieval [14][15][16][17]. To demonstrate the relations between objects and attributes in a given application domain, a concept in FCA is defined within a formal context.…”

Section: Basic Notions Of Fcamentioning

confidence: 99%

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An Entropy-Based Weighted Concept Lattice for Merging Multi-Source Geo-Ontologies

Zhu

2013

Entropy

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Abstract:To deal with the complexities associated with the rapid growth in a merged concept lattice, a formal method based on an entropy-based weighted concept lattice (EWCL) is proposed as a mechanism for merging multi-source geographic ontologies (geo-ontologies). First, formal concept analysis (FCA) is used to formalize different term-based representations in relation to the geographic domain, and to construct a merged formal context. Second, a weighted concept lattice (WCL) is applied to reduce the merged concept lattice, based on information entropy and a deviance analysis. The entropy of the attribute set is exploited to acquire the intent weight value, and the standard deviation contributes to computing the intent importance deviance value, according to the user preferences and interests. Some nodes of the merged concept lattice are then removed if their intent weights are lower than the intent importance thresholds specified by the user. Finally, experiments were conducted by combining fundamental geographic information data and spatial data in the hydraulic engineering domain from China. The results indicate that the proposed method is feasible and valid for reducing the complexities associated with the merging of geo-ontologies. Although there are still some problems in the application, the manuscript offers a new approach for the merging of geo-ontologies.

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Section: Case Study and Discussionmentioning

confidence: 99%

Section: Basic Notions Of Fcamentioning

confidence: 99%

An Entropy-Based Weighted Concept Lattice for Merging Multi-Source Geo-Ontologies

Zhu

2013

Entropy

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“…Its application has expanded into a number of fields, such as psychology, sociology, medicine, linguistics, information science, software engineering, and computer science [13][14][15]. Given a domain of interest, FCA essentially helps users build a conceptual framework of hierarchies, which is called a concept lattice, from a formal context in order to analyze, visualize, and share domain concepts and data.…”

Section: Concept and Concept Latticesmentioning

confidence: 99%

A Concept Lattice for Semantic Integration of Geo-Ontologies Based on Weight of Inclusion Degree Importance and Information Entropy

Xiao

Zhu

2016

Entropy

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Constructing a merged concept lattice with formal concept analysis (FCA) is an important research direction in the field of integrating multi-source geo-ontologies. Extracting essential geographical properties and reducing the concept lattice are two key points of previous research. A formal integration method is proposed to address the challenges in these two areas. We first extract essential properties from multi-source geo-ontologies and use FCA to build a merged formal context. Second, the combined importance weight of each single attribute of the formal context is calculated by introducing the inclusion degree importance from rough set theory and information entropy; then a weighted formal context is built from the merged formal context. Third, a combined weighted concept lattice is established from the weighted formal context with FCA and the importance weight value of every concept is defined as the sum of weight of attributes belonging to the concept's intent. Finally, semantic granularity of concept is defined by its importance weight; we, then gradually reduce the weighted concept lattice by setting up diminishing threshold of semantic granularity. Additionally, all of those reduced lattices are organized into a regular hierarchy structure based on the threshold of semantic granularity. A workflow is designed to demonstrate this procedure. A case study is conducted to show feasibility and validity of this method and the procedure to integrate multi-source geo-ontologies.

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“…Vychodil [5] Belohlavek [29,30] Hu et al [14] Djouadi [24] Ghosh et al [7] Kandasamy and Smarandache [23] Aswani Kumar [10,11,45,46,53,54] Zhang et al [32] Li and Zhang [16] Dubois and…”

Section: Formal Concept Analysis In the Fuzzy Settingmentioning

confidence: 99%

A Note on Computing the Crisp Order Context of a Fuzzy Formal Context for Knowledge Reduction

Singh¹,

Cherukuri²

2014

J Inf Process Syst

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Fuzzy Formal Concept Analysis (FCA) is a mathematical tool for the effective representation of imprecise and vague knowledge. However, with a large number of formal concepts from a fuzzy context, the task of knowledge representation becomes complex. Hence, knowledge reduction is an important issue in FCA with a fuzzy setting. The purpose of this current study is to address this issue by proposing a method that computes the corresponding crisp order for the fuzzy relation in a given fuzzy formal context. The obtained formal context using the proposed method provides a fewer number of concepts when compared to original fuzzy context. The resultant lattice structure is a reduced form of its corresponding fuzzy concept lattice and preserves the specialized and generalized concepts, as well as stability. This study also shows a step-by-step demonstration of the proposed method and its application.

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Knowledge discovery in data using formal concept analysis and random projections

Cited by 53 publications

References 27 publications

An Entropy-Based Weighted Concept Lattice for Merging Multi-Source Geo-Ontologies

An Entropy-Based Weighted Concept Lattice for Merging Multi-Source Geo-Ontologies

A Concept Lattice for Semantic Integration of Geo-Ontologies Based on Weight of Inclusion Degree Importance and Information Entropy

A Note on Computing the Crisp Order Context of a Fuzzy Formal Context for Knowledge Reduction

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