• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1055-1067
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• 2019

In view of the deficiencies of existing weighted similarity indexes, a hierarchical clustering method initialize-expand-merge (IEM) is proposed based on the similarity of common neighbors for community discovery in weighted networks. Firstly, the similarity of the node pair is defined based on the attributes of their common neighbors. Secondly, the most closely related nodes are fast clustered according to their similarity to form initial communities and expand the communities. Finally, communities are merged through maximizing the modularity so as to optimize division results. Experiments are carried out on many weighted networks, which have verified the effectiveness of the proposed algorithm. And results show that IEM is superior to weighted common neighbor (CN), weighted Adamic-Adar (AA) and weighted resources allocation (RA) when using the weighted modularity as evaluation index. Moreover, the proposed algorithm can achieve more reasonable community division for weighted networks compared with cluster-recluster-merge-algorithm (CRMA) algorithm.

• Journal of KIISE
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• v.41 no.12
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• pp.1058-1065
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• 2014

In order to assist user's who are in the process of executing a search, a query expansion method suggests keywords that are related to an input query. Recently, several studies have suggested keywords that are identified by finding domains using a clustering method over the documents that are retrieved. However, the clustering method is not relevant when presenting various domains because the number of clusters should be fixed. This paper proposes a method that suggests keywords by finding various domains related to the input queries by using a community detection algorithm. The proposed method extracts words from the top-30 documents of those that are retrieved and builds communities according to the word graph. Then, keywords representing each community are derived, and the represented keywords are used for the query expansion method. In order to evaluate the proposed method, we compared our results to those of two baseline searches performed by the Google search engine and keyword recommendation using TF-IDF in the search results. The results of the evaluation indicate that the proposed method outperforms the baseline with respect to diversity.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.4
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• pp.149-154
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• 2014

A user should pick up relevant answers by himself from various search results when using user participation question answering community like Knowledge-iN. If refined answers are automatically provided, usability of question answering community must be improved. This paper divides questions in Q&A documents into 4 types(word, list, graph and text), then proposes summarizing methods for each question type using document statistics. Summarized answers for word, list and text type are obtained by question clustering and calculating scores for words using frequency, proximity and confidence of answers. Answers for graph type is shown by extracting user opinion from answers.

• Korean Journal of Environment and Ecology
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• v.29 no.4
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• pp.599-604
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• 2015

This study was conducted the vegetation clustering analysis for the 3 mountains of Mt. Bulam, Mt. Daemo, Mt. Bonghwa which were the urban forests of Seoul. Based on the results of the analysis related to the vegetation clustering, it was found that the clustering of Mt. Bulam consisted of pine trees(Pinus densiflora)-Mongolian oak(Quercus mongolica), Hornb eam(Carpinus laxiflora)-Pitch pine(P. rigida), oriental oak(Q. variabilis) - a wild pear tree(Sorbus alnifolia) while the clustering of Mt. Daemo consisted of Pitch pine-Japanese larch(Larix leptolepis), Poplar(Populus tomentig landulosa)- black birch(Betula davurica pall). Meanwhile, the clustering of Mt. Bonghwa consisted of pine trees-a wild pear tree Community and Sawtooth oak(Q. acutissima)-Cherry Blossoms(Prunus serrulata). In relation to the similarity index by region in Mt. Bulam, Mt. Daemo, and Mt. Bonghwa, the similarity index of Mt. Bulam and Mt. Daemo stood at as high as 0.634, suggesting the distribution of similar vegetation, and the dominance index of the Mt. Daemo region was found to be 0.166 which suggests the dominance of many species compared to other regions. In addition, the results of species diversity showed that Mt. Daemo had the highest stability, and the species diversity, maximum species diversity, evenness indices were highest in Mt. Bulam, followed by Mt. Bonghwa and Mt. Daemo. The dominance index was the lowest in Mt. Bulam, followed by Mt. Bonghwa and Mt. Daemo.

• Journal of Microbiology and Biotechnology
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• v.18 no.2
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• pp.194-198
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• 2008

A newly designed Cytophaga-Flavobacteria-specific 16S rRNA gene primer pair was employed to investigate the CF community structure in the Bering Sea, revealing a previously unknown and unexpected high CF diversity in this high latitude cold sea. In total, 56 clones were sequenced and 50 unique CF 16 rRNA gene fragments were obtained, clustering into 16 CF subgroups, including nine cosmopolitan subgroups, five psychrophilic subgroups, and two putatively autochthonous subgroups. The majority of sequences (82%) were closely related to uncultured CF species and could not be classified into known CF genera, indicating the presence of a large number of so-far uncultivated CF species in the Bering Sea.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.167-176
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• 2020

Recommendation Systems is the top requirements for many people and researchers for the need required by them with the proper suggestion with their personal indeed, sorting and suggesting doctor to the patient. Most of the rating prediction in recommendation systems are based on patient's feedback with their information regarding their treatment. Patient's preferences will be based on the historical behaviour of similar patients. The similarity between the patients is generally measured by the patient's feedback with the information about the doctor with the treatment methods with their success rate. This paper presents a new method of predicting Top Ranked Doctor's in recommendation systems. The proposed Recommendation system starts by identifying the similar doctor based on the patients' health requirements and cluster them using K-Means Efficient Clustering. Our proposed K-Means Clustering with Content Based Doctor Recommendation for Cancer (KMC-CBD) helps users to find an optimal solution. The core component of KMC-CBD Recommended system suggests patients with top recommended doctors similar to the other patients who already treated with that doctor and supports the choice of the doctor and the hospital for the patient requirements and their health condition. The recommendation System first computes K-Means Clustering is an unsupervised learning among Doctors according to their profile and list the Doctors according to their Medical profile. Then the Content based doctor recommendation System generates a Top rated list of doctors for the given patient profile by exploiting health data shared by the crowd internet community. Patients can find the most similar patients, so that they can analyze how they are treated for the similar diseases, and they can send and receive suggestions to solve their health issues. In order to the improve Recommendation system efficiency, the patient can express their health information by a natural-language sentence. The Recommendation system analyze and identifies the most relevant medical area for that specific case and uses this information for the recommendation task. Provided by users as well as the recommended system to suggest the right doctors for a specific health problem. Our proposed system is implemented in Python with necessary functions and dataset.

• Hwankyungkyoyuk
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• v.16 no.1
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• pp.34-47
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• 2003

The purpose of this study is to analyze the program development process of a nonformal environmental education(EE) program in detail. For the purpose, following research questions were answered in "the Nature School in the Forest" program in Eco-Club 1) What is the program development process? 2) What is the role of staffs, program developers, in the program development process? What are the meanings of their pedagogical approach? 3) With the findings of this study, how is the researcher able to develop ground theory for community-based nonformal EE, and to promote theoretical discussion for field improvement? The data were mainly gathered through participation observation and unstructured interview. And the data were analyzed by qualitative techniques such as clustering, factoring, noting pattern and themes, seeing plausibility, making metaphors, and building logical chain of evidence. The following conclusion comes out of the findings of this study. "The Nature School in the Forest" program is a educational device which the community-based NGO chose as a strategy to change individuals and community with its ideological purpose. And the program development process was the contiuous group decision-making process among staffs and volunteers. Consequently "the Nature School in the Forest" program is a circulated process of the voluntary activists training and their participation in program operation.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.273-276
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• 2019

There is a lot of information in our world, quick access to the most accurate information or finding the information we need is more difficult and complicated. The recommendation system has become important for users to quickly find the product according to user's preference. A social recommendation system using community detection based on label propagation is proposed. In this paper, we applied community detection based on label propagation and collaborative filtering in the movie recommendation system. We implement with MovieLens dataset, the users will be clustering to the community by using label propagation algorithm, Our proposed algorithm will be recommended movie with finding the most similar community to the new user according to the personal propensity of users. Mean Absolute Error (MAE) is used to shown efficient of our proposed method.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.169-178
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• 2009

There are various approaches that retrieve information from video. However, previous approaches have considered just object information and relationship between objects without story information to retrieve contents. To retrieve exact information at video, we need analyzing approach based on characters and community since these are body of story proceeding. Therefore, this paper describes video information retrieval methodology based on character information. Characters progress story to form relationship through conversations. We can analyze the relationship between characters in a story with the methods that classifies role grades and clusters communities of characters. In this paper, for these, we propose the Character-net and describe how to classify role grades and cluster communities at Character-net. And we show this method to be efficient.

• The Journal of Bigdata
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• v.4 no.1
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• pp.99-118
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• 2019

This study used network analysis and clustering techniques to analyze studies on social media participation. As a result of the main path analysis, 37 major studies were extracted and divided into two networks: community-related networks and new media-related. Network analysis and clustering result in four clusters. This study has the academic significance of using academic data to grasp research trends at a macro level and using network analysis and machine learning as a methodology.