States, Transitions, and Life Tracks in Temporal Concept Analysis

Wolff, Karl

doi:10.1007/11528784_7

Cited by 26 publications

(24 citation statements)

References 19 publications

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“…However, the inequality A k A h does not always hold, as can be seen if we come back to the previous example and we compare the fuzzy extension A(x) = {x 1 /1, x 2 /0, x 3 /0} of the derived Lfuzzy concept in the L-fuzzy contexts (L, X,Y, R (2) ) and (L, X,Y, R (4) ) :…”

Section: The Fuzzy Context Sequence General Studymentioning

confidence: 95%

“…Now, we take the L-fuzzy context (L, X,Y, R (2) ) and obtain the L-fuzzy concepts associated with the crisp singletons {x 1 } and {x 3 } using the Lukasiewicz implication operator (I (a, b) = min(1, 1 − a + b)):…”

Section: The Fuzzy Context Sequence General Studymentioning

confidence: 99%

“…If we take h = 4 and k = 2, and the Lfuzzy contexts (L, X,Y, R (4) ) and (L, X,Y, R (2) ), then taking as a starting point the object x 1 and using the Lukasiewicz implication operator, the following Lfuzzy concepts are obtained:…”

Section: Propositionmentioning

confidence: 99%

“…However, if we take R (2) and R (2) , we have the following result: and (L, X,Y, R (2) ). Then, y 1 ∈ Persistent(x 1 ) but B 2 (y 1 ) = B (2) (y 1 ).…”

Section: Proofmentioning

confidence: 99%

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The study of fuzzy context sequences

Alcalde¹,

Burusco²,

Fuentes-González³

2013

IJCIS

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In some cases, the relationship between an object set X and an attribute set Y is set up by means of a fuzzy context sequence. A particular case of this situation appears when we want to study the evolution of an L-fuzzy context in time.In this work, we analyze these situations. First we introduce the fuzzy context sequence definition and remind the main results about OWA operators. With the aid of these operators, we propose an exhaustive study of the different contexts values of the sequence using some new relations. In the second part, we also study the fuzzy context sequences establishing tendencies and temporal patterns. Finally, we illustrate all the results by means of examples.

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Section: The Fuzzy Context Sequence General Studymentioning

confidence: 95%

Section: The Fuzzy Context Sequence General Studymentioning

confidence: 99%

Section: Propositionmentioning

confidence: 99%

“…However, if we take R (2) and R (2) , we have the following result: and (L, X,Y, R (2) ). Then, y 1 ∈ Persistent(x 1 ) but B 2 (y 1 ) = B (2) (y 1 ).…”

Section: Proofmentioning

confidence: 99%

See 2 more Smart Citations

The study of fuzzy context sequences

Alcalde¹,

Burusco²,

Fuentes-González³

2013

IJCIS

View full text Add to dashboard Cite

show abstract

“…Secondly, it is possible to identify temporal peaks of tweet frequency analyzing timestamps and exploiting the Offline Peak-Finding Algorithm (OPAD), proposed in [9]. Then, taking into account the meaning of the tweet content and time dependences among detected peaks, temporal extension of Fuzzy Formal Concept Analysis [10,11] will be performed in order to arrange tweets into a hierarchy of time dependent concepts, that is a timed fuzzy lattice. Finally, a summarization algorithm has been defined exploring resulting timed fuzzy lattice knowledge structure.…”

Section: Introductionmentioning

confidence: 99%

Time Aware Knowledge Extraction for microblog summarization on Twitter

et al. 2016

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Microblogging services like Twitter and Facebook collect millions of user generated content every moment about trending news, occurring events, and so on.Nevertheless, it is really a nightmare to find information of interest through the huge amount of available posts that are often noise and redundant. In general, social media analytics services have caught increasing attention from both side research and industry. Specifically, the dynamic context of microblogging requires to manage not only meaning of information but also the evolution of knowledge over the timeline. This work defines Time Aware Knowledge Extraction (briefly TAKE) methodology that relies on temporal extension of Fuzzy Formal Concept Analysis. In particular, a microblog summarization algorithm has been defined filtering the concepts organized by TAKE in a time-dependent hierarchy. The algorithm addresses topic-based summarization on Twitter. Besides considering the timing of the concepts, another distinguish feature of the proposed microblog summarization framework is the possibility to have more or less detailed summary, according to the user's needs, with good levels of quality and completeness as highlighted in the experimental results.

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Knowledge representation and processing with formal concept analysis

Kuznetsov

Poelmans

2013

WIREs Data Min & Knowl

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During the last three decades, formal concept analysis (FCA) became a well‐known formalism in data analysis and knowledge discovery because of its usefulness in important domains of knowledge discovery in databases (KDD) such as ontology engineering, association rule mining, machine learning, as well as relation to other established theories for representing knowledge processing, like description logics, conceptual graphs, and rough sets. In early days, FCA was sometimes misconceived as a static crisp hardly scalable formalism for binary data tables. In this paper, we will try to show that FCA actually provides support for processing large dynamical complex (may be uncertain) data augmented with additional knowledge. © 2013 Wiley Periodicals, Inc. This article is categorized under: Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Computational Intelligence

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States, Transitions, and Life Tracks in Temporal Concept Analysis

Cited by 26 publications

References 19 publications

The study of fuzzy context sequences

The study of fuzzy context sequences

Time Aware Knowledge Extraction for microblog summarization on Twitter

Knowledge representation and processing with formal concept analysis

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