• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.535-543
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• 2003

Cluster analysis has been widely used in many applications, such as pattern analysis or recognition, data analysis, image processing, market research on on-line or off-line and so on. Clustering can identify dense and sparse regions among data attributes or object attributes. But it requires many hours to get clusters that we want, because clustering is more primitive, explorative and we make many data an object of cluster analysis. In this paper we propose a new method of clustering using sample based on grid. It is more fast than any traditional clustering method and maintains its accuracy.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.77-88
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• 2003

Cluster analysis has been widely used in many applications, such that data analysis, pattern recognition, image processing, etc. But clustering requires many hours to get clusters that we want, because it is more primitive, explorative and we make many data an object of cluster analysis. In this paper we propose a new clustering method, 'Clustering algorithm using a center of gravity for grid-based sample'. It is more fast than any traditional clustering method and maintains accuracy. It reduces running time by using grid-based sample and keeps accuracy by using representative point, a center of gravity.

• Annual Conference of KIPS
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• 2008.05a
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• pp.403-406
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• 2008

Blog는 개인의 여러 미묘한 감정과 감성들을 표현하고, 이를 소통하는 Communication Channel 역할을 하고 있으며, 또한 누구나 접근할 수 있게 되었다. 이는 각 기업에게, 기존의 비효율적인 Mass Marketing 방법에서 벗어나, 소비자의 감성을 자연스럽게 추출하여 세련된 Target Marketing을 할 수 있는 훌륭한 기회를 제공하게 된다. 하지만, 고객의 Blog로 부터 미묘한 감성지수를 추출하고, 이를 마케팅 방법에 접목시키는 것은 쉽지 않은 일이다. 이러한 문제를 해결하기 위해서 본 논문에서는 고객 회원정보에 등록된 Blog를 이용하여, Target Marketing에 활용할 수 있는, 고객 Clustering을 위한 Blog 감성 추출에 대한 연구를 수행하였다. Blog의 Main Skin Image를 통해 지배적인 채도와 명도를 추출하여 수치화하고, 이를 바탕으로 고객 Blog를 테이스트 스케일법 (*일본감성연구소 개발방법)의 실증된 감성 Group 별로 Clustering 하였다. Clustering 된 각 Blog 사용자를 대상으로 연관 배색에 대한 감성 설문조사를 실시한 결과, 유의한 실험결과가 도출되어 향후 고객 감성을 기반으로 한 Target Marketing에 활용할 수 있는 가능성을 볼 수 있었다.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.2
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• pp.20-27
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• 1998

This paper describes color clustering method based on color difference in the uniform Munsell color space obtained from hue, saturation, and value. The proposed method operates in the uniform HSV color space which is approximated using ${L^*}{a^*}{b^*}$ coordinate system based on the RGB inputs. A clustering and color difference evaluation are proposed by thresholding NBS unit which is likely to Balinkin color difference equation. Region segmentation and isolation process are carried out ISO DATA algorithm which is a self iterative clustering technique. Through the clustering of 2 input images according to the threshold value, satisfactory results are obtained. So, in conclusion, it is possible to extract result of better region segmentation using human color perception of the objects.

• Smart Media Journal
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• v.10 no.3
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• pp.23-30
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• 2021

Although the manufacturing time series data clustering technique is an important grouping solution in the field of detecting and improving manufacturing large data-based equipment and process defects, it has a disadvantage of low accuracy when applying the existing static data target clustering technique to time series data. In this paper, an evolutionary computation-based time series cluster analysis approach is presented to improve the coherence of existing clustering techniques. To this end, first, the image shape resulting from the manufacturing process is converted into one-dimensional time series data using linear scanning, and the optimal sub-clusters for hierarchical cluster analysis and split cluster analysis are derived based on the Pearson distance metric as the target of the transformation data. Finally, by using a genetic algorithm, an optimal cluster combination with minimal similarity is derived for the two cluster analysis results. And the performance superiority of the proposed clustering is verified by comparing the performance with the existing clustering technique for the actual manufacturing process image.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.2
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• pp.12-17
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• 2003

This paper proposes content-based image retrieval system with fuzzy ART neural network algorithm. Retrieving large database of image data, the clustering is essential for fast retrieval. However, it is difficult to cluster huge image data pertinently, Because current retrieval methods using similarities have several problems like low accuracy of retrieving and long retrieval time, a solution is necessary to complement these problems. This paper presents a content-based image retrieval system with neural network in order to reinforce abovementioned problems. The retrieval system using fuzzy ART algorithm normalizes color and texture as feature values of input data between 0 and 1, and then it runs after clustering the input data. The implemental result with 300 image data shows retrieval accuracy of approximately 87%.

• Journal of the Korea Computer Graphics Society
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• v.12 no.4
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• pp.23-32
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• 2006

In recent years, since the digital camera has been popularized, so users can easily collect hundreds of photos in a single usage. Thus the managing of hundreds of digital photos is not a simple job comparing to the keeping paper photos. We know that managing and classifying a number of digital photo files are burdensome and annoying sometimes. So people hope to use an automated system for managing digital photos especially for their own purposes. The previous studies, e.g. content-based image retrieval, were focused on the clustering of general images, which it is not to be applied on digital photo clustering and classification. Recently, some specialized clustering algorithms for images clustering digital camera images were proposed. These algorithms exploit mainly the statistics of time gap between sequent photos. Though they showed a quite good result in image clustering for digital cameras, still lots of improvements are remained and unsolved. For example the current tools ignore completely the image transformation with the different focal lengths. In this paper, we present a photo considering focal length information recorded in EXIF. We propose an algorithms based on MVA(Matching Vector Analysis) for classification of digital images taken in the every day activity. Our experiment shows that our algorithm gives more than 95% success rates, which is competitive among all available methods in terms of sensitivity, specificity and flexibility.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.24 no.3
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• pp.321-330
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• 2020

Data classification and clustering is one of the most common applications of the machine learning. In this paper, we aim to provide the insight of the classification for Turing pattern image, which has high nonlinearity, with feature engineering using the machine learning without a multi-layered algorithm. For a given image data X whose fixel values are defined in [-1, 1], X - X³ and ∇X would be more meaningful feature than X to represent the interface and bulk region for a complex pattern image data. Therefore, we use X - X³ and ∇X in the neural network and clustering algorithm to classification. The results validate the feasibility of the proposed approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2154-2168
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• 2022

Our work addresses the issues associated with usage of the semantic features by Bag of Words model, which requires construction of the dictionary. Extracting the relevant features and clustering them into code book or dictionary is computationally intensive and requires large storage area. Hence we propose to use a simple distribution of multiple shape based features, which is a mixture of gradients, radius and slope angles requiring very less computational cost and storage requirements but can serve as an equivalent image representative. The experimental work conducted on PASCAL VOC 2007 dataset exhibits marginally closer performance in terms of accuracy with the Bag of Word model using Self Organizing Map for clustering and very significant computational gain.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.952-959
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• 2018

Clustering is one of the most fundamental algorithms in machine learning. The performance of clustering is affected by the distribution of data, and when there are more data or more dimensions, the performance is degraded. For this reason, we use a stacked auto encoder, one of the deep learning algorithms, to reduce the dimension of data which generate a feature vector that best represents the input data. We use k-means, which is a famous algorithm, as a clustering. Sine the feature vector which reduced dimensions are also multi dimensional, we use the Euclidean distance as well as the cosine similarity to increase the performance which calculating the similarity between the center of the cluster and the data as a vector. A deep clustering networks combining a stacked auto encoder and k-means re-trains the networks when the k-means result changes. When re-training the networks, the loss function of the stacked auto encoder and the loss function of the k-means are combined to improve the performance and the stability of the network. Experiments of benchmark image ad document dataset empirically validated the power of the proposed algorithm.