scholarly journals An Incremental Algorithm for Concept Lattice Based on SSIM

2021 ◽  
Author(s):  
Yu Hu ◽  
Yan Zhu Hu ◽  
Zhong Su ◽  
Xiao Li Li ◽  
Zhen Meng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Formal Concept Analysis ◽  
Time Complexity ◽  
Complexity Analysis ◽  
Concept Lattice ◽  
Incremental Algorithm ◽  
Formal Concept ◽  
Image Structure ◽  
Structure Similarity

Abstract As an effective tool for data analysis, Formal Concept Analysis (FCA) is widely used in software engineering and machine learning. The construction of concept lattice is a key step of the FCA. How to effectively update the concept lattice is still an open, interesting and important issue. The main aim of this paper is to provide a solution to this problem. So, we propose an incremental algorithm for concept lattice based on image structure similarity (SsimAddExtent). In addition, we perform time complexity analysis and experiments to show effectiveness of algorithm.

scholarly journals A New Rapid Incremental Algorithm for Constructing Concept Lattices

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 78 ◽  
Author(s):  
Jingpu Zhang ◽  
Ronghui Liu ◽  
Ligeng Zou ◽  
Licheng Zeng
Keyword(s):  
Formal Concept Analysis ◽  
Concept Lattice ◽  
Incremental Algorithm ◽  
Formal Context ◽  
Formal Concept ◽  
Concept Lattices ◽  
Covering Relation ◽  
Canonical Generator ◽  
Lattice Construction ◽  
Time And Space Complexity

Formal concept analysis has proven to be a very effective method for data analysis and rule extraction, but how to build formal concept lattices is a difficult and hot topic. In this paper, an efficient and rapid incremental concept lattice construction algorithm is proposed. The algorithm, named FastAddExtent, is seen as a modification of AddIntent in which we improve two fundamental procedures, including fixing the covering relation and searching the canonical generator. The proposed algorithm can locate the desired concept quickly by adding data fields to every concept. The algorithm is depicted in detail, using a formal context to show how the new algorithm works and discussing time and space complexity issues. We also present an experimental evaluation of its performance and comparison with AddExtent. Experimental results show that the FastAddExtent algorithm can improve efficiency compared with the primitive AddExtent algorithm.

USING FORMAL CONCEPT ANALYSIS FOR MICROARRAY DATA COMPARISON

2008 ◽  
Vol 06 (01) ◽  
pp. 65-75 ◽  
Author(s):  
V. CHOI ◽  
Y. HUANG ◽  
V. LAM ◽  
D. POTTER ◽  
R. LAUBENBACHER ◽  
...  
Keyword(s):  
Data Analysis ◽  
Microarray Data ◽  
Formal Concept Analysis ◽  
Lattice Structure ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Biological Information ◽  
Microarray Data Analysis ◽  
Mouse Lung ◽  
Formal Concept

Microarray technologies, which can measure tens of thousands of gene expression values simultaneously in a single experiment, have become a common research method for biomedical researchers. Computational tools to analyze microarray data for biological discovery are needed. In this paper, we investigate the feasibility of using formal concept analysis (FCA) as a tool for microarray data analysis. The method of FCA builds a (concept) lattice from the experimental data together with additional biological information. For microarray data, each vertex of the lattice corresponds to a subset of genes that are grouped together according to their expression values and some biological information related to gene function. The lattice structure of these gene sets might reflect biological relationships in the dataset. Similarities and differences between experiments can then be investigated by comparing their corresponding lattices according to various graph measures. We apply our method to microarray data derived from influenza-infected mouse lung tissue and healthy controls. Our preliminary results show the promise of our method as a tool for microarray data analysis.

Neutrosophic three-way concept lattice and its application in conflict analysis

2021 ◽  
pp. 1-18
Author(s):  
Han Yang ◽  
Keyun Qin
Keyword(s):  
Data Analysis ◽  
Knowledge Discovery ◽  
Formal Concept Analysis ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Conflict Analysis ◽  
Neutrosophic Set ◽  
Formal Context ◽  
Formal Concept ◽  
Concept Lattices

The theory of three-way concept analysis has been developed into an effective tool for data analysis and knowledge discovery. In this paper, we propose neutrosophic three-way concept lattice by combining neutrosophic set with three-way concept analysis and present an approach for conflict analysis by using neutrosophic three-way concept lattice. Firstly, we propose the notion of neutrosophic formal context, in which the relationships between objects and attributes are expressed by neutrosophic numbers. Three pairs of concept derivation operators are proposed. The basic properties of object-induced and attribute-induced neutrosophic three-way concept lattices are examined. Secondly, we divide the neutrosophic formal context into three classical formal contexts and propose the notions of the candidate neutrosophic three-way concepts and the redundant neutrosophic three-way concepts. Two approaches of constructing object-induced (attribute-induced) neutrosophic three-way concept lattices are presented by using candidate, redundant and original neutrosophic three-way concepts respectively. Finally, we apply the neutrosophic formal concept analysis to the conflict analysis and put forward the corresponding optimal strategy and the calculation method of the alliance.

The Method Research on Data Mining of Concept Lattice

2015 ◽  
Vol 713-715 ◽  
pp. 1970-1973
Author(s):  
Chun Liu ◽  
Dong Xing Wang ◽  
Kun Tan
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Formal Concept Analysis ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Basic Theory ◽  
Formal Concept ◽  
Concept Lattices ◽  
Hasse Diagrams ◽  
Basic Concepts

Concept lattice in essence describe the links between objects and attributes,demonstratesthe generalization and specialization of concepts. The corresponding Hasse diagrams realize the visualization of the data. At present, formal concept analysis has been extensively studied and applied to many areas, such asinformation retrieval, machine learning andsoftware engineering. Based on the above reasons, it is necessary to research the methods of latticeconcept of data mining. This paper is divided into three parts; the first part introduces the basic concepts of data mining. The second part introduces the basic theory of concept lattices. The last part focuses on the application of concept in data mining.

FCA-Based Data Analysis for Discovering Association Rules in Social Network Service

2015 ◽  
Vol 764-765 ◽  
pp. 910-914
Author(s):  
Jeong Dong Kim ◽  
Suk Hyung Hwang ◽  
Doo Kwon Baik
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Association Rules ◽  
Formal Concept Analysis ◽  
Methodological Approach ◽  
Mining Machine ◽  
Formal Concept ◽  
Data Sets ◽  
Social Network Service ◽  
Conceptual Structures

Recently, Formal Concept Analysis (FCA) have been widely used for various purposes in many different domains such as data mining, machine learning, knowledge management and so on. In this paper, we introduce FCA as the basis for a practical and well founded methodological approach for data analysis which identifies conceptual structures among data sets. As well as, we propose a FCA-based data analysis for discovering association rules by using polarity from social contents. Additionally, we show the experiments that demonstrate how our data analysis approaches can be applied for knowledge discovery by using association rules.

The Research of Semantic Web Service Matching Algorithm Based on the Formal Concept Analysis

2013 ◽  
Vol 760-762 ◽  
pp. 1708-1712
Author(s):  
Ying Fang Li ◽  
Ying Jiang Li ◽  
Yan Li ◽  
Yang Bo
Keyword(s):  
Semantic Web ◽  
Web Service ◽  
Formal Concept Analysis ◽  
Service Discovery ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Formal Concept ◽  
Semantic Web Service ◽  
Matching Algorithm ◽  
Service Matching

At present, as the number of web services resources on the network drastically increased, how to quickly and efficiently find the needed services from publishing services has become a problem to resolve. Aiming at the problems of low efficiency in service discovery of traditional web service, the formal concept analysis ( FCA) is introduced into the semantic Web service matching, and a Matching Algorithm based semantic web service is proposed. With considering the concept of limited inheritance,this method introduces the concept of limited inheritance to the semantic similarity calculation based on the concept lattice. It is significant in enhancing the service function matching in practical applications through adjust the calculation.

scholarly journals A Novel Concept Acquisition Approach Based on Formal Contexts

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ting Qian ◽  
Ling Wei
Keyword(s):  
Data Analysis ◽  
Formal Concept Analysis ◽  
Real Life ◽  
Experimental Results ◽  
New Method ◽  
Concept Acquisition ◽  
Formal Concept ◽  
Knowledge Processing ◽  
Formal Concepts ◽  
Novel Concept

As an important tool for data analysis and knowledge processing, formal concept analysis (FCA) has been applied to many fields. In this paper, we introduce a new method to find all formal concepts based on formal contexts. The amount of intents calculation is reduced by the method. And the corresponding algorithm of our approach is proposed. The main theorems and the corresponding algorithm are examined by examples, respectively. At last, several real-life databases are analyzed to demonstrate the application of the proposed approach. Experimental results show that the proposed approach is simple and effective.

scholarly journals A Web Page Clustering Method Based on Formal Concept Analysis

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 228 ◽  
Author(s):  
Zuping Zhang ◽  
Jing Zhao ◽  
Xiping Yan
Keyword(s):  
Formal Concept Analysis ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Formal Context ◽  
Formal Concept ◽  
Web Pages ◽  
Web Page ◽  
Data Links ◽  
Web Page Clustering ◽  
The Web

Web page clustering is an important technology for sorting network resources. By extraction and clustering based on the similarity of the Web page, a large amount of information on a Web page can be organized effectively. In this paper, after describing the extraction of Web feature words, calculation methods for the weighting of feature words are studied deeply. Taking Web pages as objects and Web feature words as attributes, a formal context is constructed for using formal concept analysis. An algorithm for constructing a concept lattice based on cross data links was proposed and was successfully applied. This method can be used to cluster the Web pages using the concept lattice hierarchy. Experimental results indicate that the proposed algorithm is better than previous competitors with regard to time consumption and the clustering effect.

scholarly journals Incremental Method of Generating Decision Implication Canonical Basis

2021 ◽  
Author(s):  
Shaoxia Zhang ◽  
Deyu Li ◽  
Yanhui Zhai
Keyword(s):  
Formal Concept Analysis ◽  
Canonical Basis ◽  
Concept Analysis ◽  
Experimental Results ◽  
Incremental Algorithm ◽  
Compact Representation ◽  
Formal Concept ◽  
Practical Applications ◽  
Incremental Method ◽  
Elementary Representation

Abstract Decision implication is an elementary representation of decision knowledge in formal concept analysis. Decision implication canonical basis (DICB), a set of decision implications with completeness and nonredundancy, is the most compact representation of decision implications. The method based on true premises (MBTP) for DICB generation is the most efficient one at present. In practical applications, however, data is always changing dynamically, and MBTP has to re-generate inefficiently the whole DICB. This paper proposes an incremental algorithm for DICB generation, which obtains a new DICB just by modifying and updating the existing one. Experimental results verify that when the samples in data are much more than condition attributes, which is actually a general case in practical applications, the incremental algorithm is significantly superior to MBTP. Furthermore, we conclude that, even for the data in which samples is less than condition attributes, when new samples are continually added into data, the incremental algorithm must be also more efficient than MBTP, because the incremental algorithm just needs to modify the existing DICB, which is only a part of work of MBTP.

scholarly journals Construction Search Engine Based on Formal Concept Analysis and Association Rule Mining

2012 ◽  
Vol 6-7 ◽  
pp. 625-630 ◽  
Author(s):  
Hong Sheng Xu
Keyword(s):  
Search Engine ◽  
Association Rule ◽  
Formal Concept Analysis ◽  
Association Rule Mining ◽  
High Efficiency ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Frequent Itemsets ◽  
Formal Concept ◽  
Rule Mining

In the form of background in the form of concept partial relation to the corresponding concept lattice, concept lattice is the core data structure of formal concept analysis. Association rule mining process includes two phases: first find all the frequent itemsets in data collection, Second it is by these frequent itemsets to generate association rules. This paper analyzes the association rule mining algorithms, such as Apriori and FP-Growth. The paper presents the construction search engine based on formal concept analysis and association rule mining. Experimental results show that the proposed algorithm has high efficiency.

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