• Journal of Software Engineering Society
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• v.25 no.3
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• pp.59-62
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• 2012

We propose a novel metric for quantitatively comparing different clustered results generated from software clustering algorithms. A quantitative evaluation of software clustering helps understanding of architectural changes of software. The concept of split, which has been used for analysis of genetic characters in bio-informatics, is applied in the analysis of software architecture.

• Journal of Software Engineering Society
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• v.25 no.4
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• pp.69-74
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• 2012

This article suggests applicability to community detection algorithm from module recovering process of software architecture through compare to software clustering metric and community dectection metric. in addition to, analyze mutual relation and difference between separated module and measurement value of typical clustering algorithms and community detection algorithms. and then only sugeested several kinds basis that community detection algorithm can use to recovering module view of software architecture and, by so comparing measurement value of existing clustering metric and community algorithms, this article suggested correlation of two result data.

• Journal of Korea Technology Innovation Society
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• v.5 no.2
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• pp.167-188
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• 2002

Summary: This paper analyzes Indian software industry in the perspective of innovation cluster. The research shows that the software industry has been following an upstream clustering process, where the major value activity is expanding from low value product/services to high value product/services. The growth of software industry could be successful because there was appropriate initial condition of Bangalore, such as the availability of high qualified human resources, excellent research institutes, small high-tech companies. The role of government was helpful for the late growth of software industry but not a critical factor for the initial development of the S/W cluster. It is suggested that government should consider the initial condition of a concerned location critically to implement a cluster-type innovation policy.

• The Transactions of the Korea Information Processing Society
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• v.5 no.9
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• pp.2353-2360
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• 1998

In the past. as the environment of the established soft ware system changed, most Re-Engineering perforned clustering on the basis of logical operation, In contrast, this paper proposes a method to perfonn clustering efficiently using the infonmltion sharing of each modult, of source programs that constitute the software For the clustering of related modules using the information sharing. We evaluated the result after measuring the degree of clustering using similarity and uniqueness algorithm on the basis of heuristic method of measurement. Thus, we could manipulate and achieve the clustering of related modules and procedures, This paper also prests a method to reconstruct the software system efficiently through the clustering and shows the possibility of its realization through real example.

• ETRI Journal
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• v.32 no.3
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• pp.351-361
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• 2010

Multiple rotation-based transformation (MRBT) was introduced recently for mitigating the apriori-knowledge independent component analysis (AK-ICA) attack on rotation-based transformation (RBT), which is used for privacy-preserving data clustering. MRBT is shown to mitigate the AK-ICA attack but at the expense of data utility by not enabling conventional clustering. In this paper, we extend the MRBT scheme and introduce an augmented rotation-based transformation (ARBT) scheme that utilizes linearity of transformation and that both mitigates the AK-ICA attack and enables conventional clustering on data subsets transformed using the MRBT. In order to demonstrate the computational feasibility aspect of ARBT along with RBT and MRBT, we develop a toolkit and use it to empirically compare the different schemes of privacy-preserving data clustering based on data transformation in terms of their overhead and privacy.

• ETRI Journal
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• v.30 no.6
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• pp.868-870
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• 2008

The rotation-based transformation (RBT) for privacy preserving data mining is vulnerable to the independent component analysis (ICA) attack. This paper introduces a modified multiple-rotation-based transformation technique for special mining applications, mitigating the ICA attack while maintaining the advantages of the RBT.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.4
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• pp.245-251
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• 2014

Wireless Sensor Networks(WSNs) are applied to many monitoring applications. Present sensor nodes can perform many functions at the same time and contain complex software. During the lifetime of sensor nodes, they are required to reprogram their software because of their new functions, software, software bug fixes. The nodes are inaccessible physically or it is very difficult to upgrade their software by one by one. To upgrade the software of sensor nodes in WSNs remotely, this paper presents an energy efficient method by selecting an optimal relay node. The CHR(Cluster Head Relay) method is compared with SPIN and RANDOM method. Three methods are simulated in NS-2 with the same environmental parameters. Simulation results show that CHR shows faster update time and less power consumption compared with other two methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.357-359
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• 2023

This paper proposes a hybrid recommendation system (RS) model that overcomes the limitations of traditional approaches such as data sparsity, cold start, and scalability by combining collaborative filtering and context-aware techniques. The objective of this model is to enhance the accuracy of recommendations and provide personalized suggestions by leveraging the strengths of collaborative filtering and incorporating user context features to capture their preferences and behavior more effectively. The approach utilizes a novel method that combines contextual attributes with the original user-item rating matrix of CF-based algorithms. Furthermore, we integrate k-mean++ clustering to group users with similar preferences and finally recommend items that have highly rated by other users in the same cluster. The process of partitioning is the use of the rating matrix into clusters based on contextual information offers several advantages. First, it bypasses of the computations over the entire data, reducing runtime and improving scalability. Second, the partitioned clusters hold similar ratings, which can produce greater impacts on each other, leading to more accurate recommendations and providing flexibility in the clustering process. keywords: Context-aware Recommendation, Collaborative Filtering, Kmean++ Clustering.

• Journal of KIISE
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• v.44 no.3
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• pp.275-286
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• 2017

In this study, we propose a novel technique of software clustering to recover the software module-view by using the dependency and author entropy of modules. The proposed method first performs clustering of modules based on structural and logical dependencies, then it migrates selected modules from the clustered result by utilizing the author entropy of each module. In order to evaluate the proposed method, we calculated the MoJoFM values of the recovery result by applying the method to open-source projects among which ground-truth decompositions are well-known. Compared to the MoJoFM values of previously studied techniques, we demonstrated the effectiveness of the proposed method.

• Journal of Korea Entertainment Industry Association
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• v.14 no.4
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• pp.349-354
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• 2020

This paper, firstly, propose a new Clustering scheme Based on Conceptual graphs (CBC) that can describe objects freely and can perform clustering efficiently. The conceptual clustering is one of machine learning technique. The similarity among the objects in conceptual clustering are decided on the bases of concept membership, unlike the general clustering scheme which decide the similarity without considering the context or environment of the objects. A new conceptual clustering scheme, CBC, which can perform efficient conceptual clustering by describing various objects freely with conceptual graphs is introduced in this paper.