Papers by Keyword: Concept Lattice

Abstract: This paper reveals the changesof concept collection and propertyof Hasse graph after detrimental. Detrimental construction algorithm of concept latticethis paper present is valid. In practical applications, if the property changes, you need to remove some of the properties, using the algorithm proposed in this paper can slim down directly. Based on above content, this paper is divided into three parts; the first part introduces the basic concepts of concept lattices and its basic structure. The second part introduces the basic principle of concept lattice attribute reduction and itsalgorithm. The third part is focus onincremental and detrimental algorithm analysis of node types.

Abstract: 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.

Abstract: In order to solve the multidimensional data model and relational data model,query between the two-way data system, data cleansing, data conversion, distributed data accuracy and consistency control problem, this paper described the concept of grid related, the global data mining combined with local data mining is proposed based on local information based on the concept of a global grid of data mining algorithm, and the mining process was divided into ETI. Action, combined with the ETI. Process workflow, using amounts of data distributed parallel sequence mining. Experiments show that the algorithm has a good effect on enhanced data processing capability.

Abstract: Knowledge reduction is one of the basic contents in rough set theory and the most challenging problem in knowledge acquisition. In this paper, an algorithm is proposed, which aims to get all the reducts based on the attributes of the formal context. Experiments show that the algorithm is sound and accurate. Finally, further work and future perspectives are discussed.

Abstract: Formal Concept Analysis (FCA) is well-founded mathematical theory which has been widely used for many AI needs such as software engineering, knowledge processing, ontology engineering etc. This is a survey paper in which we analyze recent literature on FCA and some closely related applications using FCA. Finally, the new trends and future perspectives are discussed.

Abstract: The paper proposes an ontology construction approach that combines Fuzzy Formal Concept Analysis, Wikipedia and WordNet in a process that constructs multiple concept lattices for sub-domains. Those sub-domains are divided from the target domain. The multiple concept lattices approach can mine concepts and determine relations between concepts automatically, and construct domain ontology accordingly. This approach is suitable for the large domain or complex domain which contains obvious sub-domains.

Abstract: With the expansion of the research field, the research object of some original seemingly unrelated properties have been studied together. At this time, the number of attribute in formal context has changed. For the increased attributes, we need to construct a new concept lattice. The existing incremental building algorithms of concept lattice need the original formal context as the basis, with single attribute or a set of attribute of the object to rebuild the concept lattice. They can't effectively utilize these existing concept lattice that have not relation in attributes. Here, the paper presents one new algorithm for incorporating concept lattice based on the existed concept lattices. We can directly build the together lattice from bottom to top by direct product operation on the existed concept lattices and the mapping relation between the direct product lattice of two existed concept lattices and the together lattice. Formal contexts that attribute sets have no intersection are fit for this algorithm.

Abstract: Concept lattice is the core data structure of formal concept analysis and represents the order relationship between the concepts iconically. Feature selection has been the focus of research in machine learning.And feature selection has been shown very effective in removing irrelevant and redundant features,also increasing efficiency in learning process and obtaining more intelligible learned results.This paper proposes a new briefest feature subset selection algorithm based on preference attribute on the basis of study of concept lattice theory. User can put forward a preference attribute according to their subjective experiences, all the briefest feature subsets containing the given attribute can be discovered by the algorithm. It firstly find some special concept pairs and calculate their waned-value hypergraph, then obtain the minimal transversal of the hypergraph as a result. A practical example proves the method is cogent and effective.

Abstract: Ontology and semantic are very popular in Web, and the construction of Web dynamic ontology has been the problem urgent to be solved. Firstly, the basic framework for constructing Web dynamic ontology is shown. Next, the concept lattice is introduced, based on which the novel method of building Web dynamic ontology is given. It includes constructing the initial ontology, extracting web knowledge, generating the formal contexts, merging formal context, merging concept lattices with ontology-based similarity calculation, transforming the concept lattice to the ontology. At last, the stock information system is employed to verify the methods proposed. The results showed the stock ontology could be dynamically updated with the change from the Web, and we can get new inferred knowledge from the updated stock ontology by Racer; the method proposed is valid and feasible.

Abstract: Semantic similarity measure has always been one of the important contents in artificial intelligence. This paper puts the ontology as the research object, and measure the semantic similarity between two ontologies in view of concept lattice. Firstly, concept lattice is introduced to similarity measure, and the thought of the ontology-based semantic similarity measure with concept lattice was given. Next, the solution of the measure was described, which includes generating a formal context of heterogeneous ontologies, constructing the corresponding formal context of formal context to fetch the formal context in concept lattice, and using irreducible infimum theory to calculate the similarity value of heterogeneous ontology concept. Finally, we employed a sample to verify the measure method. The results showed the method can effectively compute the semantic similarity between the ontologies, and the method proposed is valid and feasible.