• 한국경영과학회:학술대회논문집
• /
• 한국경영과학회 2005년도 추계학술대회 및 정기총회
• /
• pp.51-54
• /
• 2005

Clustering analysis is a descriptive task that seeks to identify homogeneous groups of objects based on the values of their attributes. In this paper we propose a new algorithm for K-medoids clustering which runs like the K-means algorithm. The new algorithm calculates distance matrix once and uses it for finding new medoids at every iterative step. We evaluate the proposed method using real and synthetic data and compare with the results of other algorithms. The proposed algorithm takes reduced time in computation and better performance than others.

• KEPCO Journal on Electric Power and Energy
• /
• 제2권1호
• /
• pp.61-69
• /
• 2016

Understanding load patterns and customer classification is a basic step in analyzing the behavior of electricity consumers. To achieve that, there have been many researches about clustering customers' daily load data. Nowadays, the deployment of advanced metering infrastructure (AMI) and big-data technologies make it easier to study customers' load data. In this paper, we study load clustering from the view point of yearly and daily load pattern. We compare four clustering methods; K-means clustering, hierarchical clustering (average & Ward's method) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). We also discuss the relationship between clustering results and Korean Standard Industrial Classification that is one of possible labels for customers' load data. We find that hierarchical clustering with Ward's method is suitable for clustering load data and KSIC can be well characterized by daily load pattern, but not quite well by yearly load pattern.

• KSBB Journal
• /
• 제16권6호
• /
• pp.538-546
• /
• 2001

효모의 세포주기 관련 유전자 발현 통합 데이터를 사용하여 본 연구실에서 개발한 유전자 발현 통합 분석프로그램을 사용하여, 클러스터링 알고리즘의 성능을 비교하고 데이터내에 존재하는 클러스터 개수를 추정하기 위해 FOM 분석을 적용하였으며, 이 분석방법을 통하여 K-means, SOM, Fuzzy c-means 클러스터링 방법의 성능을 서로 비교하였다. 클러스터 개수를 추정한 다음 3가지 클러스터링 방법에 대한 클러스터링 결과 비교, 클러스터의 기능할당 및 모티프 분석을 시도하였다. 본 논문에서 제시하는 분석 방법은 DNA chip 발현 데이터의 일반적인 분석방법을 유전자 발현 패턴의 유사성을 토대로 한 클러스터링 방법에 근간을 두고 있다. 본 논문에서는 클러스터링한 후 각 클러스터의 기능할당 및 모티프 분석에 대한 일반적인 분석방법을 제시하였으며, 본 연구실에서 개발한 유전자 발현분석 통합 프로그램이 효율적으로 사용될 수 있음을 보여주고 있다.

• 한국통계학회:학술대회논문집
• /
• 한국통계학회 2005년도 춘계 학술발표회 논문집
• /
• pp.25-29
• /
• 2005

Microarry 기술의 발전은 유전자의 기능과 상호 관련성 그리고 특성을 파악 가능하게 하였으며, 이를 위한 다양한 분석 기법들이 소개되고 있다. 본 연구에서 소개하는 fuzzy clustering 기법은 genome 영역의 expression 분석에 가장 널리 사용되는 기법중 비지도학습(unsupervized) 분석 기법이다. Fuzzy clustering 기법을 효모(yeast) expression 데이터를 이용하여 분류하여 hard k-means와 비교 하였다.

• Bulletin of the Korean Chemical Society
• /
• 제31권11호
• /
• pp.3151-3155
• /
• 2010

This study was performed to develop analytical methods to better understand the properties and reactivity of petroleum, which is a highly complex organic mixture, using high-resolution mass spectrometry and statistical analysis. Ten crude oil samples were analyzed using negative-mode electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI FT-ICR MS). Clustering methods, including principle component analysis (PCA), hierarchical clustering analysis (HCA), and k-means clustering, were used to comparatively interpret the spectra. All the methods were consistent and showed that oxygen and sulfur-containing heteroatom species played important roles in clustering samples or peaks. The oxygen-containing samples had higher acidity than the other samples, and the clustering results were linked to properties of the crude oils. This study demonstrated that clustering methods provide a simple and effective way to interpret complex petroleomic data.

• Journal of the Korean Data and Information Science Society
• /
• 제16권3호
• /
• pp.549-556
• /
• 2005

A hard-type centroids in the conventional clustering algorithm such as k-modes algorithm cannot keep the uncertainty inherently in data sets as long as possible before actual clustering(decision) are made. Therefore, we propose the k-populations algorithm to extend clustering ability and to heed the data characteristics. This k-population algorithm as found to give markedly better clustering results through various experiments.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2002년도 하계종합학술대회 논문집(3)
• /
• pp.99-102
• /
• 2002

K-means clustering algorithm is a very popular clustering technique, which is used in the field of information retrieval. In this paper, We deal with the problem of K-means Algorithm from the view of creating the centroids and suggest a method reflecting document feature and considering the context of each document to determine the new centroids during the process of forming new centroids. For experiment, We used the automatic document summarizer to summarize the Reuter21578 newslire test dataset and achieved 20% improved results to the recall metrics.

• International Journal of Computer Science & Network Security
• /
• 제21권6호
• /
• pp.237-244
• /
• 2021

Clustering is a most powerful un-supervised machine learning techniques for division of instances into homogenous group, which is called cluster. This Clustering is mainly used for generating a good quality of cluster through which we can discover hidden patterns and knowledge from the large datasets. It has huge application in different field like in medicine field, healthcare, gene-expression, image processing, agriculture, fraud detection, profitability analysis etc. The goal of this paper is to explore both hierarchical as well as partitioning clustering and understanding their problem with various approaches for their solution. Among different clustering K-means is better than other clustering due to its linear time complexity. Further this paper also focused on data mining that dealing with high-dimensional datasets with their problems and their existing approaches for their relevancy

• 한국전기전자재료학회:학술대회논문집
• /
• 한국전기전자재료학회 2007년도 추계학술대회 논문집
• /
• pp.201-202
• /
• 2007

In this paper we investigated the partial discharge distribution using the K-means clustering according to the needle of tilt and void at the cross linked polyethylene(XLPE) insulators. As a result, the specimen with tilt $45^{\circ}$ has highest breakdown voltage and the specimen with air void has lower breakdown voltage than the specimen with on void. In K-menas clustering distribution of clusters concentrates at inception condition, but the distribution spreads widely at breakdown.

• Journal of the Korean Data and Information Science Society
• /
• 제17권2호
• /
• pp.325-333
• /
• 2006

We describe tests for detecting and locating quantitative traits loci (QTL) for traits in Hanwoo. Lod scores and a permutation test have been described. From results of a permutation test to detect QTL, we select major DNA markers of BMS1167 microsatellite locus in Hanwoo chromosome 17 for further analysis. K-means clustering analysis applied to four traits and eight DNA markers in BMS1167 resulted in three cluster groups. We conclude that the major DNA markers of BMS1167 microsatellite locus in Hanwoo chromosome 17 are markers 100bp, 108bp and 110bp.