• Proceedings of the Korea Database Society Conference
• /
• 1999.06a
• /
• pp.125-133
• /
• 1999

The objective of knowledge discovery and data mining lies in the generation of useful insights from a store of data. This paper presents a framework for knowledge mining to provide a systematic approach to the selection and deployment of tools for automated learning. Every methodology has its strengths and limitations. Consequently, a multistrategy approach may be required to take advantage of the strengths of disparate technique while circumventing their individual limitations. For concreteness, the general framework for data mining in marketing is examined in the context of developing agents for optimizing a supply chain network.

• Journal for History of Mathematics
• /
• v.18 no.2
• /
• pp.95-106
• /
• 2005

This paper is investigated conception and methodology of data selection, cleaning, integration, transformation, reduction, selection and application of data mining techniques, and model evaluation during procedure of the knowledge discovery in database (KDD) based on Mathematics. Statistical role and methodology in KDD is studied as branch of Mathematics. Also, we investigate the history, mathematical background, important modeling techniques using statistics and information, practical applied field and entire examples of data mining. Also we study the differences between data mining and statistics.

• Journal of Information Technology Applications and Management
• /
• v.15 no.2
• /
• pp.1-14
• /
• 2008

The distinguished prosperity of information technologies from great progress of e-business during the last decade has unavoidably made software development for active data mining to discovery hidden predictive information regarding business trends and behavior from vary large databases. Therefore this paper develops an active mining object(ADMO) component, which provides real-time predictive information from web databases. The ADMO component is to extended ADO(ActiveX Data Object) component to active data mining component based on COM(Component Object Model) for application program interface(API). ADMO component development made use of window script component(WSC) based on XML(eXtensible Markup Language). For the purpose of investigating the application environments and the practical schemes of the ADMO component, experiments for diverse practical applications were performed in this paper. As a result, ADMO component confirmed that it could effectively extract the analytic information of classification and aggregation from vary large databases for Web services.

• East Asian Economic Review
• /
• v.23 no.4
• /
• pp.333-351
• /
• 2019

We use a data-mining bootstrap procedure to investigate the predictability test in the eight Asia-Pacific regional stock markets using in-sample and out-of-sample forecasting models. We address ourselves to the data-mining bias issues by using the data-mining bootstrap procedure proposed by Inoue and Kilian and applied to the US stock market data by Rapach and Wohar. The empirical findings show that stock returns are predictable not only in-sample but out-of-sample in Hong Kong, Malaysia, Singapore, and Korea with a few exceptions for some forecasting horizons. However, we find some significant disparity between in-sample and out-of-sample predictability in the Korean stock market. For Hong Kong, Malaysia, and Singapore, stock returns have predictable components both in-sample and out-of-sample. For the US, Australia, and Canada, we do not find any evidence of return predictability in-sample and out-of-sample with a few exceptions. For Japan, stock returns have a predictable component with price-earnings ratio as a forecasting variable for some out-of-sample forecasting horizons.

• The Korean Journal of Applied Statistics
• /
• v.15 no.2
• /
• pp.201-211
• /
• 2002

Data is being generated at an ever increasing rate in recent years, mainly due to technological advances in system architecture, processor speed, and storage structures. In this respect, data mining has attracted considerable attention and many commercial softwares for data mining have been developed. In this study, we compare the differences of functions and efficiency of application about several commercial data mining softwares which are widely used in real field.

• Communications for Statistical Applications and Methods
• /
• v.9 no.1
• /
• pp.271-280
• /
• 2002

Recently, many companies have applied data mining techniques to promote competitive power in the field of their business market. In this study, we address how data mining, that is a technique to enable to discover knowledge from a deluge of data, Is used in an executed project in order to support decision making of an enterprise. Also, we develope scoring model on customer attrition probability for automobile-insurance company using data mining techniques. The development of scoring model in domestic insurance is given as an example concretely.

• Asia pacific journal of information systems
• /
• v.8 no.3
• /
• pp.51-73
• /
• 1998

This paper is to evaluate the application potentials of data mining in the areas of Supply Chain Management (SCM) and to suggest the architectures of Decision Support Systems (DSS) that support data mining activities. We first briefly introduce data mining and review the recent literatures on SCM and then evaluate data mining applications to SCM in three aspects: marketing, operations management and information systems. By analyzing the cases about pricing models in distribution channels, demand forecasting and quality control, it is shown that artificial intelligence techniques such as artificial neural networks, case-based reasoning and expert systems, combined with traditional analysis models, effectively mine the useful knowledge from the large volume of SCM data. Agent-based information system is addressed as an important architecture that enables the pursuit of global optimization of SCM through communication and information sharing among supply chain constituents without loss of their characteristics and independence. We expect that the suggested architectures of intelligent DSS provide the basis in developing information systems for SCM to improve the quality of organizational decisions.

• Journal of the Korean Data and Information Science Society
• /
• v.19 no.4
• /
• pp.1141-1151
• /
• 2008

Data mining is the process of sorting through large amounts of data and picking out useful information. One of the well-studied problems in data mining is the exploration of association rules. An association rule technique finds the relation among each items in massive volume database. Some interestingness measures have been developed in association rule mining. Interestingness measures are useful in that it shows the causes for pruning uninteresting rules statistically or logically. This paper propose a conditional pure confidence to evaluate association rules and then describe some properties for a proposed measure. The comparative studies with confidence and pure confidence are shown by numerical example. The results show that the conditional pure confidence is better than confidence or pure confidence.

• Journal of Internet Computing and Services
• /
• v.17 no.6
• /
• pp.53-59
• /
• 2016

Recently, huge stream data have been generated in real time from various applications such as wireless sensor networks, Internet of Things services, and social network services. For this reason, to develop an efficient method have become one of significant issues in order to discover useful information from such data by processing and analyzing them and employing the information for better decision making. Since stream data are generated continuously and rapidly, there is a need to deal with them through the minimum access. In addition, an appropriate method is required to analyze stream data in resource limited environments where fast processing with low power consumption is necessary. To address this issue, the sliding window model has been proposed and researched. Meanwhile, one of data mining techniques for finding meaningful information from huge data, pattern mining extracts such information in pattern forms. Frequency-based traditional pattern mining can process only binary databases and treats items in the databases with the same importance. As a result, frequent pattern mining has a disadvantage that cannot reflect characteristics of real databases although it has played an essential role in the data mining field. From this aspect, high utility pattern mining has suggested for discovering more meaningful information from non-binary databases with the consideration of the characteristics and relative importance of items. General high utility pattern mining methods for static databases, however, are not suitable for handling stream data. To address this issue, sliding window based high utility pattern mining has been proposed for finding significant information from stream data in resource limited environments by considering their characteristics and processing them efficiently. In this paper, we conduct various experiments with datasets for performance evaluation of sliding window based high utility pattern mining algorithms and analyze experimental results, through which we study their characteristics and direction of improvement.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.23 no.3
• /
• pp.383-393
• /
• 2013

Data stream mining is a technique to obtain the useful information by analyzing the data generated in real time. In data stream mining technology, frequent itemset mining is a method to find the frequent itemset while data is transmitting, and these itemsets are used for the purpose of pattern analyze and marketing in various fields. Existing techniques of finding frequent itemset mining are having problems when a malicious attacker sniffing the data, it reveals data provider's real-time information. These problems can be solved by using a method of inserting dummy data. By using this method, a attacker cannot distinguish the original data from the transmitting data. In this paper, we propose a method for privacy preserving frequent itemset mining by using the technique of inserting dummy data. In addition, the proposed method is effective in terms of calculation because it does not require encryption technology or other mathematical operations.