• Information Systems Review
• /
• v.7 no.2
• /
• pp.23-40
• /
• 2005

Discovering customers' behavioral patterns from large data set and providing them with corresponding services or products are critical components in managing a current business. However, the diversity of customer needs coupled with the limited resources suggests that companies should make more efforts on understanding and managing specific groups of customers, not the whole customers. The key issue of this paper is based on the fact that the behavioral patterns extracted from the specific groups of customers shall be different from those from the whole customers. This paper proposes the idea of pre-segmentation before developing customer classification models. We collected three customers' demographic and transactional data sets from a credit card, a tele-communication, and an insurance company in Korea, and then segmented customers by major variables. Different churn prediction models were developed from each segments and the whole data set, respectively, using the decision tree induction approach, and compared in terms of the hit ratio and the simplicity of generated rules.

• Journal of the Korea Computer Graphics Society
• /
• v.7 no.2
• /
• pp.11-19
• /
• 2001

This paper proposes a colored paper mosaic rendering technique based on image segmentation that can automatically generate torn and tagged colored paper mosaic effect. and 3D effect that come about in human-made mosaic work can be represented by generating shadow using difference of paper thickness. Previous method did not produce satisfactory results due to the ineffectiveness of having to use pieces of the same size. The proposed two methods for determination of paper shape and location that are based on segmentation can subdivide image area by considering characteristics of image. The first method is to generate Voronoi polygon after subdividing the segmented image again using quad tree. And the second method is to apply the Voronoi diagram on each segmentation layer. Through these methods, the characteristic of the image is expressed in more detail than previous colored paper mosaic rendering method and these methods enable to produce image that is closer to human-made mosaic work.

• Korean Journal of Remote Sensing
• /
• v.27 no.3
• /
• pp.379-388
• /
• 2011

At present, GEOBIA (Geographic Object-based Image Analysis), heir of OBIA (Object-based Image Analysis), is regarded as an important methodology by object-oriented paradigm for remote sensing, dealing with geo-objects related to image segmentation and classification in the different view point of pixel-based processing. This also helps to directly link to GIS applications. Thus, GEOBIA software is on the booming. The main theme of this study is to look into the applicability of geo-spatial processing open source to GEOBIA. However, there is no few fully featured open source for GEOBIA which needs complicated schemes and algorithms, till It was carried out to implement a preliminary system for GEOBIA running an integrated and user-oriented environment. This work was performed by using various open sources such as OTB or PostgreSQL/PostGIS. Some points are different from the widely-used proprietary GEOBIA software. In this system, geo-objects are not file-based ones, but tightly linked with GIS layers in spatial database management system. The mean shift algorithm with parameters associated with spatial similarities or homogeneities is used for image segmentation. For classification process in this work, tree-based model of hierarchical network composing parent and child nodes is implemented by attribute join in the semi-automatic mode, unlike traditional image-based classification. Of course, this integrated GEOBIA system is on the progressing stage, and further works are necessary. It is expected that this approach helps to develop and to extend new applications such as urban mapping or change detection linked to GIS data sets using GEOBIA.

• Korean Business Review
• /
• v.15
• /
• pp.79-97
• /
• 2002

In this study, an analytical CRM for customer segmentation is exercised by integrating and analyzing the customer profile data and the access data to a particular web site. We believe that effective customer segmentation will be possible with a basis of the understanding of customer characteristics as well as behavior on the web. One of the critical tasks in the web data-mining is concerned with both 'how to collect the data from the web in an efficient manner?' and 'how to integrate the data(mostly in a variety of types) effectively for the analysis?' This study proposes a panel approach as an efficient data collection method in the web. For the customer data analysis, OLAF and a tree-structured algorithm are applied in this study. The results of the analysis with both techniques are compared, confirming the previous work which the two techniques are inter-complementary.

• Journal of Information Processing Systems
• /
• v.18 no.4
• /
• pp.549-561
• /
• 2022

In the Khmer writing system, the Khmer script is the official letter of Cambodia, written from left to right without a space separator; it is complicated and requires more analysis studies. Without clear standard guidelines, a space separator in the Khmer language is used inconsistently and informally to separate words in sentences. Therefore, a segmented method should be discussed with the combination of the future Khmer natural language processing (NLP) to define the appropriate rule for Khmer sentences. The critical process in NLP with the capability of extensive data language analysis necessitates applying in this scenario. One of the essential components in Khmer language processing is how to split the word into a series of sentences and count the words used in the sentences. Currently, Microsoft Word cannot count Khmer words correctly. So, this study presents a systematic library to segment Khmer phrases using the bi-directional maximal matching (BiMM) method to address these problematic constraints. In the BiMM algorithm, the paper focuses on the Bidirectional implementation of forward maximal matching (FMM) and backward maximal matching (BMM) to improve word segmentation accuracy. A digital or prefix tree of data structure algorithm, also known as a trie, enhances the segmentation accuracy procedure by finding the children of each word parent node. The accuracy of BiMM is higher than using FMM or BMM independently; moreover, the proposed approach improves dictionary structures and reduces the number of errors. The result of this study can reduce the error by 8.57% compared to FMM and BFF algorithms with 94,807 Khmer words.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.22 no.3
• /
• pp.452-458
• /
• 2013

Over the past several years, many studies have been carried out in the field of 3D data inspection systems. Several attempts have been made to improve the quality of manufactured parts. The introduction of laser sensors for inspection has made it possible to acquire data at a remarkably high speed. In this paper, a robust inspection technique for detecting defects in 3D pressed parts using laser-scanned data is proposed. Point cloud data are segmented for the extraction of features. These segmented features are used for shape matching during the localization process. An iterative closest point (ICP) algorithm is used for the localization of the scanned model and CAD model. To achieve a higher accuracy rate, the ICP algorithm is modified and then used for matching. To enhance the speed of the matching process, aKd-tree algorithm is used. Then, the deviation of the scanned points from the CAD model is computed.

• Journal of Information Processing Systems
• /
• v.16 no.5
• /
• pp.1113-1128
• /
• 2020

Ad hoc networks play an important role in mobile communications, and the performance of nodes has a significant impact on the choice of communication links. To ensure efficient and secure data forwarding and delivery, an intelligent routing protocol (IAODV) based on learning method is constructed. Five attributes of node energy, rate, credit value, computing power and transmission distance are taken as the basis of segmentation. By learning the selected samples and calculating the information gain of each attribute, the decision tree of routing node is constructed, and the rules of routing node selection are determined. IAODV algorithm realizes the adaptive evaluation and classification of network nodes, so as to determine the optimal transmission path from the source node to the destination node. The simulation results verify the feasibility, effectiveness and security of IAODV.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.3
• /
• pp.85-90
• /
• 2021

In this paper, fall detection using individual wearable devices for older people is considered. To design a low-cost wearable device for reliable fall detection, we present a comprehensive analysis of two representative models. One is a machine learning model composed of a decision tree, random forest, and Support Vector Machine(SVM). The other is a deep learning model relying on a one-dimensional(1D) Convolutional Neural Network(CNN). By considering data segmentation, preprocessing, and feature extraction methods applied to the input data, we also evaluate the considered models' validity. Simulation results verify the efficacy of the deep learning model showing improved overall performance.

• Information Systems Review
• /
• v.9 no.1
• /
• pp.67-84
• /
• 2007

In diffusing an information systems(IS), the provider of the IS can be more effective if they can identify user groups who can adopt the system early. By focusing on the user groups, system providers can encourage them to adopt the IS. After the early adopters adopt an IS, the diffusion of the system to other groups can be easier by early adopters' voluntary advertisement and help in adopting the IS. Instead of discrete choice methods which are usually used for this purpose, we suggest a decision tree method. Compared to discrete choice methods, this method is more accurate for prediction and can easily identify non-linear segments of groups. By testing the data of adopters of an IS in agricultural business, we show the excellence of this method in identifying target groups to focus on. This method would help system providers to diffuse their systems by starting from early adopters.

• The Journal of the Korea Contents Association
• /
• v.13 no.11
• /
• pp.562-570
• /
• 2013

Image of emotional pleasure or displeasure, tension or emotional division of tranquility in the form of a tree is evaluated by weighting. Image representative evaluation of the sensitivity of the brightness contrast ratings 1 car pleasure, displeasure or stress or emotional tranquility and two cars are separated by image segmentation. Emotion Recognition of four compared to the numerical data is measured by brightness. OpenCV implementation through evaluation graph the stress intensity contrast, tranquility, pleasure, displeasure, depending on changes in the value of the computing is divided into four emotional. Contrast sensitivity of computing the brightness depending on the value entered 'nuisance' to 'excellent' or 'stress' to 'calm' the emotional changes can give. Calculate the sensitivity of the image regularity of localized computing system can control the future direction of industry on the application of emotion recognition will play a positive role.