• The KIPS Transactions:PartB
• /
• v.11B no.7 s.96
• /
• pp.867-874
• /
• 2004

K-Means algorithm is a non-hierarchical (plat) and reassignment techniques and iterates algorithm steps on the basis of K cluster centroids until the clustering results converge into K clusters. In its nature, K-Means algorithm has characteristics which make different results depending on the initial and new centroids. In this paper, we propose the modified K-Means algorithm which improves the initial and new centroids decision methodologies. By evaluating the performance of two algorithms using the 16 weighting scheme of SMART system, the modified algorithm showed $20{\%}$ better results on recall and F-measure than those of K-Means algorithm, and the document clustering results are quite improved.

• Proceedings of the Korea Institute of Convergence Signal Processing
• /
• 2001.06a
• /
• pp.113-116
• /
• 2001

The conventional K-means algorithm is widely used in vector quantizer design and clustering analysis. Recently modified K-means algorithm has been proposed where the codevector updating step is as fallows: new codevector = current codevector + scale factor (new centroid - current codevector). This algorithm uses a fixed value for the scale factor. In this paper, we propose a new algorithm for the enhancement of learning time in fuzzy c-means a1gorithm. Experimental results show that the proposed method produces codebooks about 5 to 6 times faster than the conventional K-means algorithm with almost the same Performance.

• Journal of Information Processing Systems
• /
• v.4 no.2
• /
• pp.67-76
• /
• 2008

This research proposes a new strategy where documents are encoded into string vectors and modified version of k means algorithm to be adaptable to string vectors for text clustering. Traditionally, when k means algorithm is used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in text clustering, encoding full texts given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. In this research, we encode full texts into string vectors, and modify the k means algorithm adaptable to string vectors for text clustering.

• Journal of information and communication convergence engineering
• /
• v.6 no.4
• /
• pp.434-438
• /
• 2008

Massive data are continuously produced with a data rate of over several terabytes every day. These applications need effective clustering algorithms to achieve an overall high performance computation. In this paper, we propose ancestor as cluster center based approach to clustering, the K-means algorithm using ancestor. We modify the K-means algorithm. We present a clustering architecture and a clustering algorithm that minimize of I/Os and show a performance with excellent. In our experimental performance evaluation, we present that our algorithm can improve the I/O speed and the query processing time.

• The Journal of the Convergence on Culture Technology
• /
• v.7 no.2
• /
• pp.345-350
• /
• 2021

The maintenance of sensor networks, installed in a wide area has been an issue for a long time. In order to solve this problem, studies to supply energy to a sensor network using a robot has been carried out by several researchers. In this study, for a sensor network consisting of power nodes supplied with energy by multiple robots and sensor nodes around them, we propose a method of allocating a work area using a modified k-means algorithm so that the robots move the minimum distance. Through the simulation study using the energy transfer rate of the robot as a variable, it is shown that nodes of each allocated area can maintain survival, and the validity of the proposed modified k-means algorithm is verified.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.23 no.1
• /
• pp.1-16
• /
• 1998

The zone clustering problem arising from several area such as deciding the optimal location of ambient measuring stations is to devide the 2-dimensional area into several sub areas in which included individual zone shows simimlar properties. In general, the optimal solution of this problem is very hard to obtain. Therefore, instead of finding an optimal solution, the generation of near optimal solution within the limited time is more meaningful. In this study, the combination of a genetic algorithm and the modified k-means method is used to obtain the near optimal solution. To exploit the genetic algorithm effectively, a representation of chromsomes and appropriate genetic operators are proposed. The k-means method which is originally devised to solve the object clustering problem is modified to improve the solutions obtained from the genetic algorithm. The experiment shows that the proposed method generates the near optimal solution efficiently.

• International Journal of Internet, Broadcasting and Communication
• /
• v.13 no.2
• /
• pp.231-243
• /
• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• Proceedings of the Acoustical Society of Korea Conference
• /
• autumn
• /
• pp.115-118
• /
• 1999

One of the typical methods to design a codebook is K-means algorithm. This algorithm has the drawbacks that converges to a locally optimal codebook and its performance is mainly decided by an initial codebook. D. Lee's method is almost same as the K-means algorithm except for a modification of a distance value. Those methods have a fixed distance value during all iterations. After many iterations. because the distance between new codevectors and old codevectors is much shorter than the distance in the early stage of iterations, the new codevectors are not affected by distance value. But new codevectors decided in the early stage of learning iterations are much affected by distance value. Therefore it is not appropriate to fix the distance value during all iterations. In this paper, we propose a new algorithm using each different distance value between codevectors for a limited iterations in the early stage of learning iteration. In the experiment, the result show that the proposed method can design better codebooks than the conventional K-means algorithms.

• The Journal of the Acoustical Society of Korea
• /
• v.21 no.1
• /
• pp.69-72
• /
• 2002

We propose a modified splitting method to obtain an initial codebook, which is used to design a codebook. The principle of the proposed method is that the more representative vectors are assigned to the class, which has the mere member training vectors or a lower squared error. The conventional K-means algorithm and the method provided from reference (5) are used to estimate the performance of the designed codebook. In thin work, the proposed method shows better results than the conventional splitting method in all experiments.

• ETRI Journal
• /
• v.36 no.3
• /
• pp.333-342
• /
• 2014

Most hyper-ellipsoidal clustering (HEC) approaches use the Mahalanobis distance as a distance metric. It has been proven that HEC, under this condition, cannot be realized since the cost function of partitional clustering is a constant. We demonstrate that HEC with a modified Gaussian kernel metric can be interpreted as a problem of finding condensed ellipsoidal clusters (with respect to the volumes and densities of the clusters) and propose a practical HEC algorithm that is able to efficiently handle clusters that are ellipsoidal in shape and that are of different size and density. We then try to refine the HEC algorithm by utilizing ellipsoids defined on the kernel feature space to deal with more complex-shaped clusters. The proposed methods lead to a significant improvement in the clustering results over K-means algorithm, fuzzy C-means algorithm, GMM-EM algorithm, and HEC algorithm based on minimum-volume ellipsoids using Mahalanobis distance.