IRAFCA: an O(n) information retrieval algorithm based on formal concept analysis

Fkih, Fethi; Omri, Mohamed Nazih

doi:10.1007/s10115-015-0876-x

Cited by 22 publications

(6 citation statements)

References 24 publications

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“…First, the procedure Generate creates a new concept and adds the new concept to concept lattice (lines 1-2). According to Proposition 9, we test every candidate in c.Children to find real children of newConcept (lines [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Note that the concept c.children.indicator points to has already been obtained after executing the Preprocessprocedure.…”

Section: Generation and Removal Of Conceptsmentioning

confidence: 99%

“…Algorithm 1 reveals that c can be marked directly if c is a merged concept. When c is a deleted or modified concept, it requires comparisons between c andc.Parentsfor finding a parent with c.Intent = parent.Intent (lines [5][6][7][8][9][10][11][12][13]. This operation requires only one comparison in the best case or |G| comparisons at worst case between sets (intents) which takes at most O(|G||M | 2 ) time.…”

Section: Complexity Issuesmentioning

confidence: 99%

“…Each concept of a formal lattice consists of an extent and an intent, which are closely connected by the conceptual relationship. FCA focuses on obtaining different forms of outputs from original formal context for interesting information, and it has a wide range of applications in various fields including information retrieval [4]- [6], data mining [7], social network analysis [8]- [10], gene expression [11]- [13] machine learning [14], [15], ontologies [16], [17], graph mining [18]- [20]. A comprehensive survey about research trends and applications on formal concept analysis is given by Singh et al [21].…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Algorithm for Decreasing the Granularity Levels of Attributes in Formal Concept Analysis

Wan

Zou

2019

IEEE Access

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In the formal concept analysis (FCA), a concept lattice represents the basic structure derived from Boolean data describing the relationships between objects and attributes. One of the basic problems of FCA is to control the structure of concept lattices to extract useful information. To explore a data set, sometimes we need to tune the structure of the corresponding concept lattice by merging a couple of finer attributes to a coarser attribute. The merged attribute can be interpreted as a coarser granularity level. In this paper, we propose an efficient algorithm called fold for decreasing the granularity levels of attributes. We analyzed and explored the relationships between concepts before and after decreasing the granularity level of an attribute. Based on those theoretical proofs, we propose an efficient method of classifying concepts to reduce the comparisons between the concepts compared with the original zoom-out algorithm. Moreover, we provide a preprocessing procedure to search for canonical generators and help restore the covering relation. We describe the algorithm completely, discuss time complexity issues, and present an experimental evaluation of its performance and comparison with the zoom-out algorithm. The theoretical and empirical analyses demonstrate the advantages of our algorithm when applied to various types of formal contexts. INDEX TERMS Formal concept analysis, concept lattice, granularity levels of attributes, classification of concepts.

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Section: Generation and Removal Of Conceptsmentioning

confidence: 99%

Section: Complexity Issuesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient Algorithm for Decreasing the Granularity Levels of Attributes in Formal Concept Analysis

Wan

Zou

2019

IEEE Access

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“…field, among which the application under the text context (the textual formal context, that is, take text documents as objects and take the words in the documents as attributes) has a promising future [9]. Just as the recognition of semantic similar concept becomes a fundamental technological component in many fields, such as cognitive science, artificial intelligence, and semantic web, the similarity measurement of formal concept serves as the basis of almost all FCA-based data processing applications and plays a vital role [10]- [15].…”

Section: Introductionmentioning

confidence: 99%

A Similarity Measure in Formal Concept Analysis Containing General Semantic Information and Domain Information

Wang

Cai

et al. 2020

IEEE Access

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Formal concept analysis (FCA) gets into good graces by increasing big data scientists due to its unique advantages. Concept similarity measurement is the key to the FCA-based application. Most of the previous methods are based on set theory and less concerned with semantic information, whereas those methods focusing on semantic information usually rely on ontologies or knowledge bases to obtain the relevant semantic knowledge. However, it is difficult for knowledge methods to obtain domain knowledge in formal contexts (datasets), which are not suited well for domain text data. To tackle these problems, this paper proposes a novel formal concept similarity measure that synthesizes the Semantic information in knowledge bases and Domain information in the formal context (S&D measure). S&D uses word vectors as the representations of words to obtain the semantic information in general knowledge bases while defining novel semantic relations of intent words to obtain the domain information contained in the data itself. It can measure the similarity relation of concepts more comprehensively and precisely, particularly in a domain textual formal context, and it can be implemented automatically and unsupervisedly without any knowledge base, ontology or external corpus. Compared with other related works, experiments show that this method has a better correlation with human judgment.

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“…The line diagram corresponding to a concept lattice vividly unfolds generalization/specialization relationship among concepts [2]. Recently, concept lattices have already been successfully applied to a wide range of scientific disciplines including knowledge representation [3][4][5], knowledge discovery [6][7][8], knowledge reduction [9][10][11], hybrid relation analysis [12], wireless sensor network [13], and information retrieval [14].…”

Section: Introductionmentioning

confidence: 99%

The Hierarchies of Multivalued Attribute Domains and Corresponding Applications in Data Mining

Lei

Yan

Han

et al. 2018

Wireless Communications and Mobile Computing

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In mobile computing, machine learning models for natural language processing (NLP) have become one of the most attractive focus areas in research. Association rules among attributes are common knowledge patterns, which can often provide potential and useful information such as mobile users' interests. Actually, almost each attribute is associated with a hierarchy of the domain. Given an relation R=(U,A) and any cut αa on the hierarchy for every attribute a, there is another rough relation RΦ, where Φ=(αa:a∈A). This paper will establish the connection between the functional dependencies in R and RΦ, propose the method for extracting reducts in RΦ, and demonstrate the implementation of proposed method on an application in data mining of association rules. The method for acquiring association rules consists of the following three steps: (1) translating natural texts into relations, by NLP; (2) translating relations into rough ones, by attributes analysis or fuzzy k-means (FKM) clustering; and (3) extracting association rules from concept lattices, by formal concept analysis (FCA). Our experimental results show that the proposed methods, which can be applied directly to regular mobile data such as healthcare data, improved quality, and relevance of rules.

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IRAFCA: an O(n) information retrieval algorithm based on formal concept analysis

Cited by 22 publications

References 24 publications

An Efficient Algorithm for Decreasing the Granularity Levels of Attributes in Formal Concept Analysis

An Efficient Algorithm for Decreasing the Granularity Levels of Attributes in Formal Concept Analysis

A Similarity Measure in Formal Concept Analysis Containing General Semantic Information and Domain Information

The Hierarchies of Multivalued Attribute Domains and Corresponding Applications in Data Mining

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