• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.410-411
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• 2006

This paper is focused on the survey on the power system modeling using a clustering algorithm. In electricity markets, clustering method is a efficient tool to model the power system. It can be seen that electricity markets can also be classified into several groups which show similar patterns and that the fundamental characteristics of power systems can be widely applicable to other technical problems in power system such as generation scheduling, power flow analysis, short-term load forecasting, and so on. There are several researches on the power system modeling using a clustering algorithm. We specially surveyed their own clustering methods to model the power system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1419-1436
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• 2020

For an energy harvesting sensor network, when the network lifetime is not the only primary goal, maximizing the network performance under environmental energy harvesting becomes a more critical issue. However, clustering protocols that aim at providing maximum information throughput have not been thoroughly explored in Energy Harvesting Wireless Sensor Networks (EH-WSNs). In this paper, clustering protocols are studied for maximizing the data transmission in the whole network. Based on a long short-term memory (LSTM) energy predictor and node energy consumption and supplement models, an uneven clustering protocol is proposed where the cluster head selection and cluster size control are thoroughly designed for this purpose. Simulations and results verify that the proposed scheme can outperform some classic schemes by having more data packets received by the cluster heads (CHs) and the base station (BS) under these energy constraints. The outcomes of this paper also provide some insights for choosing clustering routing protocols in EH-WSNs, by exploiting the factors such as uneven clustering size, number of clusters, multiple CHs, multihop routing strategy, and energy supplementing period.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.111-125
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• 2011

Clustering is a process of grouping similar or relevant documents into a cluster and assigning a meaningful concept to the cluster. By this process, clustering facilitates fast and correct search for the relevant documents by narrowing down the range of searching only to the collection of documents belonging to related clusters. For effective clustering, techniques are required for identifying similar documents and grouping them into a cluster, and discovering a concept that is most relevant to the cluster. One of the problems often appearing in this context is the detection of a complex concept that overlaps with several simple concepts at the same hierarchical level. Previous clustering methods were unable to identify and represent a complex concept that belongs to several different clusters at the same level in the concept hierarchy, and also could not validate the semantic hierarchical relationship between a complex concept and each of simple concepts. In order to solve these problems, this paper proposes a new clustering method that identifies and represents complex concepts efficiently. We developed the Hierarchical Overlapping Clustering (HOC) algorithm that modified the traditional Agglomerative Hierarchical Clustering algorithm to allow overlapped clusters at the same level in the concept hierarchy. The HOC algorithm represents the clustering result not by a tree but by a lattice to detect complex concepts. We developed a system that employs the HOC algorithm to carry out the goal of complex concept detection. This system operates in three phases; 1) the preprocessing of documents, 2) the clustering using the HOC algorithm, and 3) the validation of semantic hierarchical relationships among the concepts in the lattice obtained as a result of clustering. The preprocessing phase represents the documents as x-y coordinate values in a 2-dimensional space by considering the weights of terms appearing in the documents. First, it goes through some refinement process by applying stopwords removal and stemming to extract index terms. Then, each index term is assigned a TF-IDF weight value and the x-y coordinate value for each document is determined by combining the TF-IDF values of the terms in it. The clustering phase uses the HOC algorithm in which the similarity between the documents is calculated by applying the Euclidean distance method. Initially, a cluster is generated for each document by grouping those documents that are closest to it. Then, the distance between any two clusters is measured, grouping the closest clusters as a new cluster. This process is repeated until the root cluster is generated. In the validation phase, the feature selection method is applied to validate the appropriateness of the cluster concepts built by the HOC algorithm to see if they have meaningful hierarchical relationships. Feature selection is a method of extracting key features from a document by identifying and assigning weight values to important and representative terms in the document. In order to correctly select key features, a method is needed to determine how each term contributes to the class of the document. Among several methods achieving this goal, this paper adopted the $x^2$�� statistics, which measures the dependency degree of a term t to a class c, and represents the relationship between t and c by a numerical value. To demonstrate the effectiveness of the HOC algorithm, a series of performance evaluation is carried out by using a well-known Reuter-21578 news collection. The result of performance evaluation showed that the HOC algorithm greatly contributes to detecting and producing complex concepts by generating the concept hierarchy in a lattice structure.

• Journal of Information Management
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• v.35 no.3
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• pp.125-138
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• 2004

The purpose of this study is to propose a hierarchical user interfaces for browsing the knowledge structure of a single document. To generate the hierarchical knowledge structure, hierarchical term clustering and cluster representative term selection were performed with a single thesis in information science field, and the result was applied to design the interfaces which browse a single document hierarchically using multidimensional scaling. The interfaces can be applied to develop the user-friendly information retrieval system.

• International Commerce and Information Review
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• v.7 no.4
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• pp.245-268
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• 2005

The purpose of this paper is to acquire competitiveness faced with a global business so that Korea and China make them put ICT into practice through industrial policy of regional innovation clustering. In the Chapter 2, overall review of industrial spaces theory and the environment in Global-business is conducted. In the Chapter 3, current main economic issue and West Great Development of China are viewed. Chapter 4 proposes models and strategies for the target of regional innovation clustering and phasing in development. The results of this study is that both country should do more long-term cooperation and collecting intensive knowledge for the property of region and preparatory research of regional innovation clustering than do reckless investment.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.06a
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• pp.330-335
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• 2002

The most critical issue in information retrieval system is to have adequate results corresponding to user requests. When all documents related with user inquiry retrieve, it is not easy not only to find correct document what user wants but is limited. Therefore, clustering method that grouped by corresponding documents has widely used so far. In this paper, we cluster on the basis of the meaning rather than the index term in the existing document and a LSI method is applied by this reason. Furthermore, we distinguish and analyze differences from the clustering using widely-used K-Means algorithm for the document clustering.

• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1327-1339
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• 2023

World Wide Web (WWW) use has been increasing recently due to users needing more information. Lately, there has been a growing trend in the document information available to end users through the internet. The web's document search process is essential to find relevant documents for user queries.As the number of general web pages increases, it becomes increasingly challenging for users to find records that are appropriate to their interests. However, using existing Document Information Retrieval (DIR) approaches is time-consuming for large document collections. To alleviate the problem, this novel presents Spatial Clustering Ranking Pattern (SCRP) based Density Ant Colony Information Retrieval (DACIR) for user queries based DIR. The proposed first stage is the Term Frequency Weight (TFW) technique to identify the query weightage-based frequency. Based on the weight score, they are grouped and ranked using the proposed Spatial Clustering Ranking Pattern (SCRP) technique. Finally, based on ranking, select the most relevant information retrieves the document using DACIR algorithm.The proposed method outperforms traditional information retrieval methods regarding the quality of returned objects while performing significantly better in run time.

• Journal of Korean Library and Information Science Society
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• v.34 no.2
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• pp.233-253
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• 2003

Knowledge organization is the way we humans understand the world. There are two types of information organization mechanisms studied in information retrieval: namely classification md clustering. Classification organizes entities by pigeonholing them into predefined categories, whereas clustering organizes information by grouping similar or related entities together. The system of the Internet information resources extracts a keyword from the words which appear in the web document and draws up a reverse file. Term clustering based on grouping related terms, however, did not prove overly successful and was mostly abandoned in cases of documents used different languages each other or door-way-pages composed of only an anchor text. This study examines infometric analysis and clustering possibility of web documents based on co-link topology of web pages.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2785-2799
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• 2020

We propose a novel speaker adaptation method using acoustic model clustering. The similarity of different speakers is defined by the cosine distance between their i-vectors (intermediate vectors), and various efficient clustering algorithms are applied to obtain a number of speaker subsets with different characteristics. The speaker-independent model is then retrained with the training data of the individual speaker subsets grouped by the clustering results, and an unknown speech is recognized by the retrained model of the closest cluster. The proposed method is applied to a large-scale speech recognition system implemented by a hybrid hidden Markov model and deep neural network framework. An experiment was conducted to evaluate the word error rates using Resource Management database. When the proposed speaker adaptation method using i-vector based clustering was applied, the performance, as compared to that of the conventional speaker-independent speech recognition model, was improved relatively by as much as 12.2% for the conventional fully neural network, and by as much as 10.5% for the bidirectional long short-term memory.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.149-154
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• 2006

The main purpose of document summarization is to reduce the complexity of documents that are consisted of sub-themes. Also it is to create summarization which includes the sub-themes. This paper proposes a summarization system which could extract any salient sentences in accordance with sub-themes by using graph division. A document can be represented in graphs by using chosen representative terms through term relativity analysis based on co-occurrence information. This graph, then, is subdivided to represent sub-themes through connected information. The divided graphs are types of sentence clustering which shows a close relationship. When salient sentences are extracted from the divided graphs, summarization consisted of core elements of sentences from the sub-themes can be produced. As a result, the summarization quality will be improved.