Performance Analysis of Clustering using Partitioning and Hierarchical Clustering Techniques

S. C. Punitha,P. Ranjith Jeba Thangaiah,M. Punithavalli 보안공학연구지원센터 2014 International Journal of Database Theory and Appli Vol.7 No.6

Text clustering is the method of combining text or documents which are similar and dissimilar to one another. In several text tasks, this text mining is used such as extraction of information and concept/entity, summarization of documents, modeling of relation with entity, categorization/classification and clustering. This text mining categorizes only digital documents or text and it is a method of data mining. It is the method of combining text document into category and applied in various applications such as retrieval of information, web or corporate information systems. Clustering is also called unsupervised learning because like other document classification, no labeled documents are providing in clustering; hence, clustering is also known as unsupervised learning. A new method called Hierarchical Agglomerative Clustering (HAC) which manages clusters as tree like structure that make possible for browsing. In this HAC method, the nodes in the tree can be viewed as parent-child relationship i.e. topic-subtopic relationship in a hierarchy. HAC method starts with each example in its own cluster and iteratively combines them to form larger and larger clusters. The main focus of this work is to present a performance analysis of various techniques available for document clustering.

[정보분석 서비스] 자질 선택과 군집 점수 최대화를 이용한 대체 클러스터링

Tao Thanh Vinh,이종혁 한국정보과학회 2012 정보과학회 컴퓨팅의 실제 논문지 Vol.18 No.9

본 논문은 자질 선택과 군집 점수 최대화를 이용하여 대체 클러스터를 찾는 방법론을 제안한다. 우선 대체 클러스터를 찾기 위하여 중요한 자질을 찾고, 분류된 자질을 기준으로 대상 데이터를 변환하였으며, 대체 클러스터의 분류 품질을 높이기 위하여 군집 점수를 최대화하는 알고리즘을 적용하였다. UCI 데이터를 사용하여 실험한 결과, 제안한 방법론이 JI와 DI를 기준으로 한 평가에서 가장 높은 성능을 보였다. 본 결과는 자질 선택과 군집 점수의 최대화가 대체 클러스터를 찾는데 유용하다는 것을 보여준다. We proposed a method for finding alternative clusterings of a dataset based on feature selection and direct maximization of clustering quality. We found the possible important features for the alternative clustering. We transformed the data using these features to make the original clustering not likely to be found while the alternative clustering is more likely to be found; we used a clustering algorithm that directly maximize the clustering quality so that the alternative clustering will be high quality. We tested our approach with some other approaches on our synthetic dataset, UCI datasets: Segmentation, Vehicle, Vowel, Ionosphere and Glass, and a textual dataset. Our approach was the most stable one as it resulted in the best JI and DI for most of the tests. Our results showed that feature selection and direct maximization of clustering quality are important for finding alternative clusterings.

협업적 여과 시스템의 성능 향상을 위한 장르 패턴 기반 사용자 클러스터링

최자현(Ja-Hyun Choi),하인애(In-Ay Ha),홍명덕(Myung-Duk Hong),조근식(Geun-Sik Jo) 한국컴퓨터정보학회 2011 韓國컴퓨터情報學會論文誌 Vol.16 No.11

협업적 여과 시스템은 사용자에 대한 클러스터링을 구축한 후, 구축된 클러스터를 기반으로 사용자에게 아이템을 추천한다. 그러나 사용자 클러스터링 구축에 많은 시간이 소요되고, 사용자가 평가한 아이템이 피드백 되었을 경우 재구축이 쉽지 않다. 본 논문에서는 영화 추천 시스템에서의 사용자 클러스터링의 재구축 시간을 단축시키기 위해서 빈발 패턴 네트워크를 이용하여 사용자가 선호하는 장르 패턴을 추출하고, 추출된 패턴을 통해 사용자 클러스터링을 구축한다. 구축된 사용자 클러스터링을 협업적 여과에 적용하여 사용자에게 영화를 추천한다. 사용자 정보가 피드백 될 때, 전통적 협업적 여과는 사용자 클러스터링을 재구축하기 위해 모든 이웃 사용자를 재탐색하여 클러스터링 한다. 하지만 빈발 패턴 네트워크를 이용하여 장르 패턴 기반의 사용자 클러스터링을 적용한 협업적 여과는 사용자 클러스터링을 재구축시 사용자 탐색 공간을 국한시킴으로써 탐색 시간을 줄일 수 있다. 제안하는 장르 패턴기반의 사용자 클러스터링을 통해 사용자 정보가 피드백 된 후 사용자 클러스터를 재구축시 소요되는 시간을 줄일 수 있고, 전통적인 협업적 여과 시스템과 유사한 성능의 추천이 가능하게 되었다. Collaborative filtering system is the clustering about user is built and then based on that clustering results will recommend the preferred item to the user. However, building user clustering is time consuming and also once the users evaluate and give feedback about the film then rebuilding the system is not simple. In this paper, genre pattern of movie recommendation systems is being used and in order to simplify and reduce time of rebuilding user clustering. A Frequent pattern networks is used and then extracts user preference genre patterns and through that extracted patterns user clustering will be built. Through built the clustering for all neighboring users to collaborative filtering is applied and then recommends movies to the user. When receiving user information feedback, traditional collaborative filtering is to rebuild the clustering for all neighbouring users to research and do the clustering. However by using frequent pattern Networks, through user clustering based on genre pattern, collaborative filtering is applied and when rebuilding user clustering inquiry limited by search time can be reduced. After receiving user information feedback through proposed user clustering based on genre pattern, the time that need to spent on re-establishing user clustering can be reduced and also enable the possibility of traditional collaborative filtering systems and recommendation of a similar performance.

DBSCAN 클러스터링을 적용한 인천광역시 PMS Data 상관성 분석

이수형,이재훈,김연태,정진훈 한국도로학회 2022 한국도로학회논문집 Vol.24 No.6

PURPOSES : Local governments in Korea, including Incheon city, have introduced the pavement management system (PMS). However, the verification of the repair time and repair section of roads remains difficult owing to the non-existence of a systematic data acquisition system. Therefore, data refinement is performed using various techniques when analyzing statistical data in diverse fields. In this study, clustering is used to analyze PMS data, and correlation analysis is conducted between pavement performance and influencing factors. METHODS : First, the clustering type was selected. The representative clustering types include K-means, mean shift, and density-based spatial clustering of applications with noise (DBSCAN). In this study, data purification was performed using DBSCAN for clustering. Because of the difficulty in determining a threshold for high-dimensional data, multiple clustering, which is a type of DBSCAN, was applied, and the number of clustering was set up to two. Clustering for the surface distress (SD), rut depth (RD), and international roughness index (IRI) was performed twice using the number of frost days, the highest temperature, and the average temperature, respectively. RESULTS : The clustering result shows that the correlation between the SD and number of frost days improved significantly. The correlation between the maximum temperature factor and precipitation factor, which does not indicate multicollinearity, improved. Meanwhile, the correlation between the RD and highest temperature improved significantly. The correlation between the minimum temperature factor and precipitation factor, which does not exhibit multicollinearity, improved considerably. The correlation between the IRI and average temperature improved as well. The correlation between the low- and high-temperature precipitation factors, which does not indicate multicollinearity, improved. CONCLUSIONS : The result confirms the possibility of applying clustering to refine PMS data and that the correlation among the pavement performance factors improved. However, when applying clustering to PMS data refinement, the limitations must be identified and addressed. Furthermore, clustering may be applicable to the purification of PMS data using AI.

주파수 및 시간 특성을 활용한 머신러닝 기반 공동주택 주거소음의 군집화 및 분류

김정훈,이송미,김수홍,송은성,류종관 한국음향학회 2023 韓國音響學會誌 Vol.42 No.6

본 연구는 주파수 및 시간 특성을 활용하여 머신러닝 기반 공동주택 주거소음의 군집화 및 분류를 진행하였다. 먼저, 공동주택 주거소음의 군집화 및 분류를 진행하기 위하여 주거소음원 데이터셋을 구축하였다. 주거소음원 데이터셋은 바닥충격음, 공기전달음, 급배수 및 설비소음, 환경소음, 공사장 소음으로 구성되었다. 각 음원의 주파수 특성은 1/1과 1/3 옥타브 밴드별 Leq와 Lmax값을 도출하였으며, 시간적 특성은 5 s 동안의 6 ms 간격의 음압레벨 분석을 통해Leq값을 도출하였다. 공동주택 주거소음원의 군집화는 K-Means clustering을 통해 진행하였다. K-Means의 k의 개수는 실루엣 계수와 엘보우 방법을 통해 결정하였다. 주파수 특성을 통한 주거소음원 군집화는 모든 평가지수에서 3개로군집되었다. 주파수 특성 기준으로 분류된 각 군집별 시간적 특성을 통한 주거소음원 군집화는 Leq평가지수의 경우 9 개, Lmax 경우는 11개로 군집되었다. 주파수 특성을 통해 군집된 각 군집은 타 주파수 대역 대비 저주파 대역의 음에너지의 비율 또한 조사되었다. 이후, 군집화 결과를 활용하기 위한 방안으로 세 종류의 머신러닝 방법을 이용해 주거소음을 분류하였다. 주거소음 분류 결과, 1/3 옥타브 밴드의 Leq값으로 라벨링된 데이터에서 가장 높은 정확도와 f1-score 가 나타났다. 또한, 주파수 및 시간적 특성을 모두 사용하여 인공신경망(Artificial Neural Network, ANN) 모델로 주거소음원을 분류했을 때 93 %의 정확도와 92 %의 f1-score로 가장 높게 나타났다. In this study, machine learning-based clustering and classification of residential noise in apartment buildings was conducted using frequency and temporal characteristics. First, a residential noise source dataset was constructed . The residential noise source dataset was consisted of floor impact, airborne, plumbing and equipment noise, environmental, and construction noise. The clustering of residential noise was performed by K-Means clustering method. For frequency characteristics, Leq and Lmax values were derived for 1/1 and 1/3 octave band for each sound source. For temporal characteristics, Leq values were derived at every 6 ms through sound pressure level analysis for 5 s. The number of k in K-Means clustering method was determined through the silhouette coefficient and elbow method. The clustering of residential noise source by frequency characteristic resulted in three clusters for both Leq and Lmax analysis. Temporal characteristic clustered residential noise source into 9 clusters for Leq and 11 clusters for Lmax. Clustering by frequency characteristic clustered according to the proportion of low frequency band. Then, to utilize the clustering results, the residential noise source was classified using three kinds of machine learning. The results of the residential noise classification showed the highest accuracy and f1-score for data labeled with Leq values in 1/3 octave bands, and the highest accuracy and f1-score for classifying residential noise sources with an Artificial Neural Network (ANN) model using both frequency and temporal features, with 93 % accuracy and 92 % f1-score.

China Coal Industry International Competitiveness Research Based on Unascertained Clustering

Xiang Chen,Yang Liu,Yuxia Liang,Xin Zhao 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.6

The unascertained clustering is a new clustering method, which combines unascertained theory and clustering theory to construct the unascertained measure, and uses the unascertained measure as set membership to indicate the membership relation between the samples with the different classes. It overcomes the disadvantage of means clustering algorithm, that a sample definitely belongs to a class, which made greater progress than -means clustering. There are complex non-linear relationship between the coal industry competitiveness and various factors. The article established the evaluation influencing factors system of coal industry international competitiveness. 6 unascertained clustering method to cluster competitiveness. It found out each class center, and gave the membership degree of the samples belong to each class, which better resolved the problem of classifying the coal industry international competitiveness.

Arabic Text Clustering Methods and Suggested Solutions for Theme-Based Quran Clustering: Analysis of Literature

Bsoul, Qusay,Abdul Salam, Rosalina,Atwan, Jaffar,Jawarneh, Malik Korea Institute of Science and Technology Informat 2021 Journal of Information Science Theory and Practice Vol.9 No.4

Text clustering is one of the most commonly used methods for detecting themes or types of documents. Text clustering is used in many fields, but its effectiveness is still not sufficient to be used for the understanding of Arabic text, especially with respect to terms extraction, unsupervised feature selection, and clustering algorithms. In most cases, terms extraction focuses on nouns. Clustering simplifies the understanding of an Arabic text like the text of the Quran; it is important not only for Muslims but for all people who want to know more about Islam. This paper discusses the complexity and limitations of Arabic text clustering in the Quran based on their themes. Unsupervised feature selection does not consider the relationships between the selected features. One weakness of clustering algorithms is that the selection of the optimal initial centroid still depends on chances and manual settings. Consequently, this paper reviews literature about the three major stages of Arabic clustering: terms extraction, unsupervised feature selection, and clustering. Six experiments were conducted to demonstrate previously un-discussed problems related to the metrics used for feature selection and clustering. Suggestions to improve clustering of the Quran based on themes are presented and discussed.

Some Clustering-Based Methodology Applications to Anomaly Intrusion Detection Systems

Veselina Jecheva,Evgeniya Nikolova 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.1

The present paper introduces some clustering-based methodology applications to the anomaly and host-based intrusion detection. The proposed methodologies include fuzzy clustering, fuzzy clustering by local approximation of memberships and 2-means clustering algorithms. The presented anomaly-based frameworks are evaluated by simulation experiments and comparison of the obtained results.

어휘 군집 방법이 중학교 영어 학습자의 어휘 습득에 미치는 영향

하봄이 ( Ha Bomyi ),윤현숙 ( Yoon Hyunsook ) 한국외국어대학교 외국어교육연구소 2018 외국어교육연구 Vol.32 No.3

본 연구의 목적은 다양한 어휘 군집 방법이 중학교 영어 학습자의 어휘 학습에 미치는 영향과 학습자가 다양한 어휘 군집 방법의 효과에 대해 가지는 기대 및 인식과 실제 효과와의 관계를 비교하고자 하는 데 있다. 이를 위해 32명의 학습자가 4가지 군집방법, 즉, 무작위군, 의미군, 두 가지의 주제군 군집방법에 참여하였다. 주제군은 학습자의 주제 친숙도에 따라 주제군1과 주제군2로 나뉘어 진행되었다. 연구 결과, 즉각적인 학습 효과와 장기적인 학습효과 모두에서 의미군을 제외하고 무작위군, 주제군1, 주제군2의 학습 효과가 비슷한 수준으로 나타났다. 하지만, 의미군에서는 통계적으로 유의미한 수준의 낮은 학습효과가 발견되었다. 또한, 학습자의 주제 친숙도는 학습효과에 별다른 영향을 미치지 않았으며, 학습자들이 다양한 어휘군집 방법의 학습효과에 대해 갖는 기대와 인식도 실제 학습효과와는 무관한 것으로 나타났다. 이 결과를 바탕으로 어휘 교수에 대한 제언이 제시된다. This study aimed to investigate the effects of different word clustering on middle school English learners’ vocabulary acquisition and the relationship between the learners’ perceptions on the effectiveness of different types of word clustering and actual test scores. A total of 32 students participated in vocabulary learning sessions with four different types of word clustering: unrelated, semantic, and two types of thematic clustering. The two thematic clusters were selected based on the degree of learners’ familiarity with topics. The results showed that the three types of clustering, which are unrelated and two types of thematic clustering, were equally effective, but semantic clustering was less effective than the other three types in both short-term and long-term vocabulary learning. Also, topic familiarity resulted in no significant difference in the effectiveness of vocabulary learning, and no meaningful relationship was found between the learners’ perception on the effectiveness of clustering types and the actual test scores. Implications for teaching vocabulary are discussed based on the results.

K-Means Clustering of Shakespeare Sonnets with Selected Features

T. Senthil Selvi,R. Parimala 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.8

This paper focuses on clustering the lines of Shakespeare Sonnets. Sonnet Line Clustering (SLC) is the task of grouping a set of lines in such a way that lines in the same cluster are more similar to each other than to those in other clusters. K-Means clustering is a very effective clustering technique well known for its observed speed and its simplicity. Its aim is to find the best division of N lines into K groups (clusters), so that the total distance between the groups’s members and corresponding centroid, is minimized. A new algorithm Sonnet Line Clustering with Random Feature Selection SLCRFS is proposed. To validate the process external validation or internal validation is done. Since, internal validation has no considerable impact in conducting research this work concentrates on the measures of external validation. Entropy and Purity are frequently used external measures of validation for K-Means. The proposed approach uses entropy as performance measure. The clusters formed are evaluated and interpreted according to the Euclidean measure between data points and cluster centers of each cluster. This paper concludes with an analysis of the results of using the proposed measure to display the clustered sonnets using K-Means algorithm with minimum entropy for different feature sets.