• 말소리와 음성과학
• /
• 제1권2호
• /
• pp.3-8
• /
• 2009

In an effort to improve speech recognition, we investigated the effect of the number of clusters. In usual LBG clustering, the number of codebook clusters is doubled on each bifurcation and hence cannot be chosen arbitrarily in a natural way. To have the number of clusters at our control, we combined adaptive resonance theory (ART2) with LBG and perform the clustering in two stages. The codebook thus formed was used in subsequent processing of fuzzy vector quantization (FVQ) and HMM for speech recognition tests. Compared to conventional LBG, our method was shown to reduce the best recognition error rate by 0${\sim$}0.9% depending on the vocabulary size. The result also showed that between 400 and 800 would be the optimal number of clusters in the limit of small and large vocabulary speech recognitions of isolated words, respectively.

• 한국정보과학회:학술대회논문집
• /
• 한국정보과학회 2006년도 가을 학술발표논문집 Vol.33 No.2 (B)
• /
• pp.233-237
• /
• 2006

This study proposes an innovative measure for evaluating the performance of text clustering. In using K-means algorithm and Kohonen Networks for text clustering, the number clusters is fixed initially by configuring it as their parameter, while in using single pass algorithm for text clustering, the number of clusters is not predictable. Using labeled documents, the result of text clustering using K-means algorithm or Kohonen Network is able to be evaluated by setting the number of clusters as the number of the given target categories, mapping each cluster to a target category, and using the evaluation measures of text. But in using single pass algorithm, if the number of clusters is different from the number of target categories, such measures are useless for evaluating the result of text clustering. This study proposes an evaluation measure of text clustering based on intra-cluster similarity and inter-cluster similarity, what is called CI (Clustering Index) in this article.

• Journal of the Korean Statistical Society
• /
• 제29권2호
• /
• pp.247-260
• /
• 2000

This paper is concerned with a modification of the k-spatial medians clustering. To find a suitable number of clusters, the number k of clusters is incorporated into the k-spatial medians clustering criterion through a weight function. Proposed method for the choice of the weight function offers a reasonable number of clusters. Some theoretical properties of the method are investigated along with some examples.

• 전자공학회논문지CI
• /
• 제41권6호
• /
• pp.39-46
• /
• 2004

클러스터 분석에 있어 중요한 문제 중의 하나는 주어진 데이터에 내재된 적절한 클러스터의 수를 찾아내는 것이다. 본 논문에서는 이러한 클러스터의 개수를 체계적으로 결정하기 위하여 IRC (Input Representation Coverage) 개념을 새로이 정의하고, 이를 이용하여 주어진 데이터에 적합한 클러스터의 개수를 자동 결정하는 방법을 제시한다. 또한, 이러한 방법의 유용성 및 응용성을 알아보기 위하여 가상 데이터를 가지고 분석 실험을 하였으며, 실험을 통해 데이터에 내재된 실제 클러스터의 개수를 찾아내는 데에 제안된 방법이 매우 유용하게 사용될 수 있음을 보여준다.

• International journal of advanced smart convergence
• /
• 제7권2호
• /
• pp.33-37
• /
• 2018

In this study, we propose an iterative clustering algorithm that automatically clusters a set of voice signal data without a label into an optimal number of clusters and generates hmm model for each cluster. In the clustering process, the likelihood calculations of the clusters are performed using iterative hmm learning and testing while varying the number of clusters for given data, and the maximum likelihood estimation method is used to determine the optimal number of clusters. We tested the effectiveness of this clustering algorithm on a small-vocabulary digit clustering task by mapping the unsupervised decoded output of the optimal cluster to the ground-truth transcription, we found out that they were highly correlated.

• 한국경영과학회지
• /
• 제31권2호
• /
• pp.87-98
• /
• 2006

In many cases of cluster analysis we are forced to perform clustering without any prior knowledge on the number of clusters. But in some clustering methods such as k-means algorithm it is required to provide the number of clusters beforehand. In this study, we focus on the problem to determine the number of clusters in the given data. We follow the 2 stage approach of ASA clustering algorithm and mainly try to improve the performance of the first stage of the algorithm. We verify the usefulness of the method by applying it for various kinds of simulated data. Also, we apply the method for clustering two kinds of real life qualitative data.

• 디지털콘텐츠학회 논문지
• /
• 제12권3호
• /
• pp.339-345
• /
• 2011

군집화는 주어진 데이터를 분할하여 데이터 속에 숨겨져 있는 의미를 자동으로 발견하는 방법이다. k-means는 간단하고 빠른 군집화 알고리즘 중의 하나이다. 군집의 수 k는 군집화를 수행하는데 매우 중요한 요소이며, k의 값에 의해 군집화 결과가 달라진다. 본 논문에서는 반복적인 k-means 수행과 군집의 품질을 평가하는 외부 군집 연관 기준 정보를 결합하여 최적의 군집수를 결정하는 방법을 제안한다. 실험 결과 기존의 방법들에 비하여 제안하는 방법이 군집수의 정확성 측면에서 우수한 성능을 보였다.

• 한국경영과학회지
• /
• 제42권3호
• /
• pp.25-34
• /
• 2017

K-means is a popular and efficient data clustering method that only uses intra-cluster distance to establish a valid index with a previously fixed number of clusters. K-means is useless without a suitable number of clusters for unsupervised data. This paper aimsto propose the Group Search Optimization (GSO) using Silhouette to find the optimal data clustering solution with a number of clusters for unsupervised data. Silhouette can be used as valid index to decide the number of clusters and optimal solution by simultaneously considering intra- and inter-cluster distances. The performance of GSO using Silhouette is validated through several experiment and analysis of data sets.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제6권1호
• /
• pp.10-14
• /
• 2006

Many clustering methods have been studied. For the most part of these methods may be needed to determine the number of clusters. But, there are few methods for determining the number of population clusters objectively. It is difficult to determine the cluster size. In general, the number of clusters is decided by subjectively prior knowledge. Because the results of clustering depend on the number of clusters, it must be determined seriously. In this paper, we propose an efficient method for determining the number of clusters using hybrid' self organizing map and new criterion for evaluating the clustering result. In the experiment, we verify our model to compare other clustering methods using the data sets from UCI machine learning repository.

• 한국전자통신학회논문지
• /
• 제9권11호
• /
• pp.1221-1226
• /
• 2014

본 논문에서는 음성 인식의 효율을 높이기 위하여 음소 클러스터 개수의 효과에 대해 연구하였다. 이를 위하여 음소 클러스터 개수를 바꾸어 가면서 수정된 k-평균 군집 알고리듬을 사용하여 코우드북을 작성하였다. 그런 다음, 퍼지 벡터 양자화와 은닉 마코브 모델을 사용하여 음성인식 테스트를 수행하였다. 실험 결과 두 개의 영역이 구분되어 나타났다. 음소 클러스터 개수가 클 때 인식 성능은 대체로 그와 무관하지만, 개수가 작을 때에는 그 감소와 더불어 인식 오류율이 비선형적으로 증가하는 것으로 나타났다. 수치 해석적 계산으로부터, 이 비선형 영역은 멱승함수에 의해 모델링 될 수 있었다. 또한 300개의 고립단어 인식의 경우에, 166개의 음소클러스터가 최적의 수임을 보일 수 있었다. 이는 음소당 3개 정도의 변화에 해당하는 값이다.