An Approach Using Concept Lattice Structure for Data Mining and Information Retrieval

2015 ◽  
Vol 1 ◽  
pp. 1
Author(s):  
Tran Lam Quan ◽  
Vu Tat Thang
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Text Mining ◽  
Formal Concept Analysis ◽  
Mathematical Theory ◽  
Lattice Structure ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Frequent Itemset ◽  
Formal Concept

Since the 1980s, the concept lattice was studied and applied to the problems of text mining, frequent itemset, classification, etc. The formal concept analysis - FCA is one of the main techniques applied in the concept lattice. FCA is a mathematical theory which is applied to the data mining by setting a table with rows describing objects and columns describing attributes, with relationships between them, and then sets up the concept lattice structure. In the area of information retrieval, FCA considers the correlation of objects-attributes the same as those of documents-terms. In the process of setting up the lattice, FCA defines each node in the lattice as a concept. The algorithm for the construction of concept lattice will install a couple on each node, including a set of documents with common terms, and a set of terms which co-occurs in documents. In a larger scale, each concept in the lattice could be recognized as a couple of questions - answers. In the lattice, the action of browsing up or down of nodes will allow approaching more general concepts or more detail concepts, respectively.

scholarly journals Efficient Closure Operators for FCA-Based Classification

2020 ◽  
Vol 10 (2) ◽  
pp. 79-98
Author(s):  
Nida Meddouri ◽  
Mondher Maddouri
Keyword(s):  
Data Mining ◽  
Formal Concept Analysis ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Nearest Neighbor Search ◽  
Knowledge Discovery In Databases ◽  
Support Vector ◽  
Formal Concept ◽  
Closure Operators ◽  
Nominal Data

Knowledge discovery in databases (KDD) aims to exploit the large amounts of data collected every day in various fields of computing application. The idea is to extract hidden knowledge from a set of data. It gathers several tasks that constitute a process, such as: data selection, pre-processing, transformation, data mining, visualization, etc. Data mining techniques include supervised classification and unsupervised classification. Classification consists of predicting the class of new instances with a classifier built on learning data of labeled instances. Several approaches were proposed such as: the induction of decision trees, Bayes, nearest neighbor search, neural networks, support vector machines, and formal concept analysis. Learning formal concepts always refers to the mathematical structure of concept lattice. This article presents a state of the art on formal concept analysis classifier. The authors present different ways to calculate the closure operators from nominal data and also present new approach to build only a part of the lattice including the best concepts. This approach is based on Dagging (ensemble method) that generates an ensemble of classifiers, each one represents a formal concept, and combines them by a voting rule. Experimental results are given to prove the efficiency of the proposed method.

Interactive Data Mining for Large-Scale Image Databases Based on Formal Concept Analysis

2010 ◽  
Vol 14 (3) ◽  
pp. 303-308 ◽  
Author(s):  
Takanari Tanabata ◽  
◽  
Kazuhito Sawase ◽  
Hajime Nobuhara ◽  
Barnabas Bede ◽  
...  
Keyword(s):  
Data Mining ◽  
Formal Concept Analysis ◽  
Large Scale ◽  
Lattice Structure ◽  
Concept Analysis ◽  
Image Features ◽  
Image Databases ◽  
Formal Concept ◽  
Interactive Data Mining ◽  
Interactive Data

In order to perform an interactive data-mining for huge image databases efficiently, a visualization interface based on Formal Concept Analysis (FCA) is proposed. The proposed interface system provides an intuitive lattice structure enabling users freely and easily to select FCA attributes and to view different aspects of the Hasse diagram of the lattice of a given image database. The investigation environment is implemented using C++ and the OpenCV library on a personal computer (CPU = 2.13 GHz, MM = 2 GB). In visualization experiments using 1,000 Corel Image Gallery images, we test image features such as color, edge, and face detectors as FCA attributes. Experimental analysis confirms the effectiveness of the proposed interface and its potential as an efficient datamining tool.

Knowledge Representation Using Formal Concept Analysis

2014 ◽  
pp. 306-336 ◽  
Author(s):  
Ch. Aswani Kumar ◽  
Prem Kumar Singh
Keyword(s):  
Formal Concept Analysis ◽  
Lattice Structure ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Formal Context ◽  
Formal Concept ◽  
Mathematical Framework ◽  
Formal Concepts ◽  
Conceptual Data ◽  
The Relationship

Introduced by Rudolf Wille in the mid-80s, Formal Concept Analysis (FCA) is a mathematical framework that offers conceptual data analysis and knowledge discovery. FCA analyzes the data, which is represented in the form of a formal context, that describe the relationship between a particular set of objects and a particular set of attributes. From the formal context, FCA produces hierarchically ordered clusters called formal concepts and the basis of attribute dependencies, called attribute implications. All the concepts of a formal context form a hierarchical complete lattice structure called concept lattice that reflects the relationship of generalization and specialization among concepts. Several algorithms are proposed in the literature to extract the formal concepts from a given context. The objective of this chapter is to analyze, demonstrate, and compare a few standard algorithms that extract the formal concepts. For each algorithm, the analysis considers the functionality, output, complexity, delay time, exploration type, and data structures involved.

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.

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.

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 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 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.

The construction of multi-granularity concept lattices

2020 ◽  
Vol 39 (3) ◽  
pp. 2783-2790
Author(s):  
Qian Hu ◽  
Ke-Yun Qin
Keyword(s):  
Formal Concept Analysis ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Formal Context ◽  
Formal Concept ◽  
Mutual Transformation ◽  
Concept Lattices ◽  
Construction Methods ◽  
Formal Concepts ◽  
Transformation Methods

The construction of concept lattices is an important research topic in formal concept analysis. Inspired by multi-granularity rough sets, multi-granularity formal concept analysis has become a new hot research issue. This paper mainly studies the construction methods of concept lattices in multi-granularity formal context. The relationships between concept forming operators under different granularity are discussed. The mutual transformation methods of formal concepts under different granularity are presented. In addition, the approaches of obtaining coarse-granularity concept lattice by fine-granularity concept lattice and fine-granularity concept lattice by coarse-granularity concept lattice are examined. The related algorithms for generating concept lattices are proposed. The practicability of the method is illustrated by an example.

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