• Journal of the Korean Data and Information Science Society
• /
• v.16 no.1
• /
• pp.59-69
• /
• 2005

Data mining is the method to find useful information for large amounts of data in database It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. We analyze Gyeongnam social indicator survey data by 2001 using association rule technique for environment information. Association rule mining searches for interesting relationships among items in a given large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. We can use to environmental preservation and environmental improvement by association rule outputs

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2001.06a
• /
• pp.253-260
• /
• 2001

Understanding and adapting to changes of customer behavior is an important aspect for a company to survive in continuously changing environment. The aim of this paper is to develop a methodology which detects changes of customer behavior automatically from customer profiles and sales data at different time snapshots. For this purpose, we first define three types of changes as emerging pattern, unexpected change and the added / perished rule. Then we develop similarity and difference measures for rule matching to detect all types of change. Finally, the degree of change is evaluated to detect significantly changed rules. Our proposed methodology can evaluate degree of changes as well as detect all kinds of change automatically from different time snapshot data. A case study for evaluation and practical business implications for this methodology are also provided.

• Journal of Internet Computing and Services
• /
• v.10 no.5
• /
• pp.65-78
• /
• 2009

The most commonly adopted approach to find valuable information from tree data is to extract frequently occurring subtree patterns from them. Because mining frequent tree patterns has a wide range of applications such as xml mining, web usage mining, bioinformatics, and network multicast routing, many algorithms have been recently proposed to find the patterns. However, existing tree mining algorithms suffer from several serious pitfalls in finding frequent tree patterns from massive tree datasets. Some of the major problems are due to (1) modeling data as hierarchical tree structure, (2) the computationally high cost of the candidate maintenance, (3) the repetitious input dataset scans, and (4) the high memory dependency. These problems stem from that most of these algorithms are based on the well-known apriori algorithm and have used anti-monotone property for candidate generation and frequency counting in their algorithms. To solve the problems, we base a pattern-growth approach rather than the apriori approach, and choose to extract maximal frequent subtree patterns instead of frequent subtree patterns. The proposed method not only gets rid of the process for infrequent subtrees pruning, but also totally eliminates the problem of generating candidate subtrees. Hence, it significantly improves the whole mining process.

• Economic and Environmental Geology
• /
• v.27 no.1
• /
• pp.11-28
• /
• 1994

The Anyang Feldspar Mine is located in Seoksu Dong, Anyang City, Kyeonggi Do, Korea and has a long exploitation record that is once produced high grade sodium feldspars, for glaze. Geologically, This area is mainly composed of Mesozoic Jurassic biotite granite (Anyang granite) which intruded Precambrian Kyeonggi Gneiss Complex outcroped near the mining area. The deposit is localized on the southwest hill side of Anyang granite batholith and is confined in hydrothemal alteration zone formed by sodium-rich alkali hydrothermal fluids along the fractures of leucocratic granite showing later differentiation facies in the biotite granite. The hydrothermal alteration is characterized by albitization, sericitization, and desilication. The microscopic observation and EPMA, XRD analysis of the feldspar ores show that major minerals are albite and quartz and accessory minerals are orthoclase and sericite, and they are rarely associated with perthite, fluorite, zircon, kaolinite, molybdenite, microcline and iron-oxide. In the REE pattern, the strong negative Eu anomalies of the feldspar ores indicate the influence of feldspar fractionation and show similiar pattern of the host leucocratic granite. The filling temperature of quartz crystals in ore zone ranges from $276^{\circ}C$ to $342^{\circ}C$, and it is inferred that the alteration occurred by the hypothermal solution.

• The KIPS Transactions:PartA
• /
• v.17A no.4
• /
• pp.181-188
• /
• 2010

In this study, we suggest the system to analyze network users' action patterns by using Data-Mining Technique. We installed Network Tap to implement the analysis system of network action and copied the network packet. The copied packet is stored at the database through MainMemoryDB(MMDB) of the high-speed. The stored data analyze the users' action patterns by using Data-Mining Technique and then report the results to the network manager on real-time. Also, we applied the standard XML document exchange method to share the data between different systems. We propose this action pattern analysis system operated embedded type of SetToBox to install easily and support low price.

• International Journal of Computer Science & Network Security
• /
• v.21 no.9
• /
• pp.31-40
• /
• 2021

The increasing number of credit card fraud cases has become a considerable problem since the past decades. This phenomenon is due to the expansion of new technologies, including the increased popularity and volume of online banking transactions and e-commerce. In order to address the problem of credit card fraud detection, a rule-based approach has been widely utilized to detect and guard against fraudulent activities. However, it requires huge computational power and high complexity in defining and building the rule base for pattern matching, in order to precisely identifying the fraud patterns. In addition, it does not come with intelligence and ability in predicting or analysing transaction data in looking for new fraud patterns and strategies. As such, Data Mining and Machine Learning algorithms are proposed to overcome the shortcomings in this paper. The aim of this paper is to highlight the important techniques and methodologies that are employed in fraud detection, while at the same time focusing on the existing literature. Methods such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), naïve Bayesian, k-Nearest Neighbour (k-NN), Decision Tree and Frequent Pattern Mining algorithms are reviewed and evaluated for their performance in detecting fraudulent transaction.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.13 no.2
• /
• pp.99-106
• /
• 2003

Intrusions pose a serious security risk in a network environment. For detecting the intrusion effectively, many researches have developed data mining framework for constructing intrusion detection modules. Traditional anomaly detection techniques focus on detecting anomalies in new data after training on normal data. To detect anomalous behavior, Precise normal Pattern is necessary. This training data is typically expensive to produce. For this, the understanding of the characteristics of data on network is inevitable. In this paper, we propose to use clustering and association rules as the basis for guiding anomaly detection. For applying entropy to filter noisy data, we present a technique for detecting anomalies without training on normal data. We present dynamic transaction for generating more effectively detection patterns.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.3
• /
• pp.155-164
• /
• 2016

For frequency pattern analysis of large databases, the new tree-based frequency pattern analysis algorithm which can compensate for the disadvantages of the Apriori method has been variously studied. In frequency pattern tree, the number of nodes is associated with memory allocation, but also affects memory resource consumption and processing speed of the growth. Therefore, reducing the number of nodes in the tree is very important in the frequency pattern mining. However, the absolute criteria which need to order the transaction items for construction frequency pattern tree has lowered the compression ratio of the tree nodes. But most of the frequency based tree construction methods adapted the absolute criteria. FP-tree is typically frequency pattern tree structure which is an extended prefix-tree structure for storing compressed frequent crucial information about frequent patterns. For construction the tree, all the frequent items in different transactions are sorted according to the absolute criteria, frequency descending order. CanTree also need to absolute criteria, canonical order, to construct the tree. In this paper, we proposed a novel frequency pattern tree construction method that does not use the absolute criteria, IRFP-tree algorithm. IRFP-tree(Intersection Rule based FP-tree). IRFP-tree is constituted with the new paradigm of the intersection rule without the use of the absolute criteria. It increased the compression ratio of the tree nodes, and reduced the tree construction time. Our method has the additional advantage that it provides incremental mining. The reported test result demonstrate the applicability and effectiveness of the proposed approach.

• Journal of Korea Society of Industrial Information Systems
• /
• v.21 no.3
• /
• pp.35-46
• /
• 2016

Social network services (SNS) that help to build relationship network and share a particular interest or activity freely according to their interests by posting comments, photos, videos,${\ldots}$ on online communities such as blogs have adopted and developed widely as a social phenomenon. Several researches have been done to explore the pattern and valuable information in social networks data via text mining such as opinion mining and semantic analysis. For improving the efficiency of text mining, keyword-based approach have been applied but most of researchers argued the limitations of the rules of Korean orthography. This research aims to construct a database of non-standard Korean words which are difficulty in data mining such abbreviations, slangs, strange expressions, emoticons in order to improve the limitations in keyword-based text mining techniques. Based on the study of subjective opinions about specific topics on blogs, this research extracted non-standard words that were found useful in text mining process.

• Korean Journal of Health Education and Promotion
• /
• v.26 no.4
• /
• pp.1-10
• /
• 2009

Objectives: The purpose of this study is to find the determinants of suicide impulse of residents living in mining region and other areas in one city. The past studies did not examine the suicide related attitudes or behaviors in mining region. This study also examines how coping resources and behaviors moderate the suicide impulse. Methods: For this purpose, hierarchical logistic regression method was used to predict the likelihood of suicide impulse. The personal characteristics, depression, coping resources and behaviors were considered as the independent variables. The data collected in this study was gathered through questionnaire survey with 502 residents in other areas as well as mining area in one city. Results and Conclusion: The results and conclusions are as follows: 1. The chi-square test revealed that residents living mining region showed higher percentage of suicide impulse compared to other areas. 2. The t-test revealed that those with suicide impulse had higher level of depression compared to those without it. This pattern was consistent in other areas as well as mining region. 3. The hierarchical logistic regression revealed that age, education, depression showed positive effect on suicide impulse in mining region. However, in other areas, education, illness, and depression showed positive effect on suicide impulse. Also, this result implies that suicide prevention efforts should be actively made in mining region.