• International Journal of Contents
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• v.2 no.1
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• pp.22-28
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• 2006

In this paper, we propose new semantic contents modeling using individual features, associative relations and visual features for efficiently supporting browsing and retrieval of video semantic contents. And we implement and design a browsing and retrieval system based on the semantic contents modeling. The browsing system supports annotation based information, keyframe based visual information, associative relations, and text based semantic information using a tree based browsing technique. The retrieval system supports text based retrieval, visual feature and associative relations according to the retrieval types of semantic contents.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.1-8
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• 2007

In electronic commerce, it is important for users to recommend the proper item among large item sets with saving time and effort. Therefore, if the recommendation system can be recommended the suitable item, we will gain a good satisfaction to the user. In this paper, we proposed the associative relation clustering split method in the collaborative filtering in order to perform the accuracy and the scalability. We produce the lift between associative items using the ratings data. and then split the node group that consists of the item to improve an efficiency of the associative relation cluster. This method differs the association about the items of groups. If the association of groups is filled, the reminding items combine. To estimate the performance, the suggested method is compared with the K-means and EM in the MovieLens data set.

• The Journal of the Korea Contents Association
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• v.8 no.6
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• pp.26-33
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• 2008

The customer relation marketing in which companies can utilize to control and to get the filtered information efficiently has appeared in ubiquitous commerce. It is applying data mining technique to build the management that can even predict and recommend products to customers. In this paper, we proposed the case of customer relation management application using the associative mining. The proposed method uses the associative mining composes frequent customers with occurrence of candidate customer-set creates the association rules. We analyzed the efficient the feature of purchase customers using the hypergraph partition according to the lift of creative association rules. Therefore, we discovered strategies of the cross-selling and the up-selling. To estimate the performance, the suggested method is compared with the existing methods in the questionnaire dataset. The results have shown that the proposed method significantly outperforms the accuracy than the previous methods.

• Honam Mathematical Journal
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• v.28 no.4
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• pp.559-572
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• 2006

The notion of simulative and mutant WFI-algebras is introduced, and several properties are investigated. Characterizations of a simulative WFI-algebra are established. A relation between an associative WFI-algebra and a simulative WFI-algebra is given. Some types for a simulative WFI-algebra to be mutant are found.

• Kyungpook Mathematical Journal
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• v.55 no.3
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• pp.515-530
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• 2015

We say that (R, f, g) is an additive (m, n)-semihyperring if R is a non-empty set, f is an m-ary associative hyperoperation, g is an n-ary associative operation and g is distributive with respect to f. In this paper, we describe a number of characterizations of additive (m, n)-semihyperrings which generalize well-known results. Also, we consider distinguished elements, hyperideals, Rees factors and regular relations. Later, we give a natural method to derive the quotient (m, n)-semihyperring.

• Journal of KIISE:Databases
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• v.31 no.3
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• pp.287-296
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• 2004

Recently, various location-based services are becoming very popular in mobile environments. In this paper, we propose a new concept of a frequent item set, called “associative class set”, for supporting the location-based service which uses a large quantity of a spatial database in mobile computing environments, and then present a new method for efficiently generating the associative class set. The associative class set is generated with considering the temporal relation of queries, the spatial distance of required objects, and access patterns of users. The result of our research can play a fundamental role in efficiently supporting location-based services and in overcoming the limitation of mobile environments. The associative class set can be applied by a recommendation system of a geographic information system in mobile computing environments, mobile advertisement, city development planning, and client cache police of mobile users.

• Korean Journal of Mathematics
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• v.23 no.4
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• pp.557-569
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• 2015

We dene a fuzzy lattice as a fuzzy relation, develop some basic properties of the fuzzy lattice, show that the operations of join and meet in fuzzy lattices are isotone and associative, characterize a fuzzy lattice by its level set, and show that the direct product of two fuzzy lattices is a fuzzy lattice.

• Journal of applied mathematics & informatics
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• v.10 no.1_2
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• pp.51-62
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• 2002

The present paper gives a new construction of a quotient BCI(BCK)-algebra X/${\mu}$ by a fuzzy ideal ${\mu}$ in X and establishes the Fuzzy Homomorphism Fundamental Theorem. We show that if ${\mu}$ is a fuzzy ideal (closed fuzzy ideal) of X, then X/${\mu}$ is a commutative (resp. positive implicative, implicative) BCK(BCI)-algebra if and only if It is a fuzzy commutative (resp. positive implicative, implicative) ideal of X Moreover we prove that a fuzzy ideal of a BCI-algebra is closed if and only if it is a fuzzy subalgebra of X We show that if the period of every element in a BCI-algebra X is finite, then any fuzzy ideal of X is closed. Especiatly, in a well (resp. finite, associative, quasi-associative, simple) BCI-algebra, any fuzzy ideal must be closed.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.467-477
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• 2004

While recommender systems were used by a few E-commerce sites former days, they are now becoming serious business tools that are re-shaping the world of I-commerce. And collaborative filtering has been a very successful recommendation technique in both research and practice. But there are two problems in personalized recommender systems, it is First-Rating problem and Sparsity problem. In this paper, we solve these problems using the associative relation clustering and “Lift” of association rules. We produce “Lift” between items using user's rating data. And we apply Threshold by -cut to the association between items. To make an efficiency of associative relation cluster higher, we use not only the existing Hypergraph Clique Clustering algorithm but also the suggested Split Cluster method. If the cluster is completed, we calculate a similarity iten in each inner cluster. And the index is saved in the database for the fast access. We apply the creating index to predict the preference for new items. To estimate the Performance, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• The Pure and Applied Mathematics
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• v.25 no.4
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• pp.253-265
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• 2018

In this paper, we introduce the concept of Fibonacci sequences on MV-algebras and study them accurately. Also, by introducing the concepts of periodic sequences and power-associative MV-algebras, other properties are also obtained. The relation between MV-algebras and Fibonacci sequences is investigated.