• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.673-682
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• 2009

XML data are widely used for data representation and exchange on the Web and the data type is an continuous stream in ubiquitous environment. Therefore there are some mining researches related to the extracting of frequent structures and the efficient query processing of XML stream data. In this paper, we propose a mining method to extract frequent structures of XML stream data in recent window based on the sliding window. XML stream data are modeled as a tree set, called XFP_tree and we quickly extract the frequent structures over recent XML data in the XFP_tree.

• The KIPS Transactions:PartD
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• v.19D no.2
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• pp.139-146
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• 2012

Data Mining is widely used to discover knowledge in many fields. Although there are many methods to discover association rule, most of them are based on frequency-based approaches. Therefore it is not appropriate for stream environment. Because the stream environment has a property that event data are generated continuously. it is expensive to store all data. In this paper, we propose a new method to discover association rules based on stream environment. Our new method is using a variable window for extracting data items. Variable windows have variable size according to the gap of same target event. Our method extracts data using COBJ(Count object) calculation method. FPMDSTN(Frequent pattern Mining over Data Stream using Terminal Node) discovers association rules from the extracted data items. Through experiment, our method is more efficient to apply stream environment than conventional methods.

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

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.335-344
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• 2005

Recently open data mining methods focusing on a data stream that is a massive unbounded sequence of data elements continuously generated at a rapid rate are proposed actively. Knowledge embedded in a data stream is likely to be changed over time. Therefore, identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. This paper proposes a sliding window technique for finding recently frequent itemsets, which is applied efficiently in open data mining. In the proposed technique, its memory usage is kept in a small space by delayed-insertion and pruning operations, and its mining result can be found in a short time since the data elements within its target range are not traversed repeatedly. Moreover, the proposed technique focused in the recent data elements, so that it can catch out the recent change of the data stream.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.9
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• pp.4183-4191
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• 2011

Considering the characteristics of a data stream whose data elements are continuously generated and may change over time, there have been many techniques to differentiate the importance of data elements in a data stream by their generation time. The conventional techniques are efficient to get an analysis result focusing on the recent information in a data stream, but they have a limitation to differentiate the importance of information in various ways more flexible. An information differentiation technique based on the term of a fuzzy set can be an alternative way to compensate the limitation. A term of a fuzzy set has been widely used in various data mining fields, which can overcome the sharp boundary problem and give an analysis result reflecting the requirements in real world applications more. In this paper, a fuzzy window mechanism is proposed, which is adapting a term of a fuzzy set and is efficiently used to differentiate the importance of information in mining data streams. Basic concepts including fuzzy calendars are described first, and subsequently details on data stream mining of weighted patterns using a fuzzy window technique are described.

• Journal of Information Processing Systems
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• v.6 no.1
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• pp.79-90
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• 2010

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Data mining over data streams should support the flexible trade-off between processing time and mining accuracy. In many application areas, mining frequent itemsets has been suggested to find important frequent itemsets by considering the weight of itemsets. In this paper, we present an efficient algorithm WSFI (Weighted Support Frequent Itemsets)-Mine with normalized weight over data streams. Moreover, we propose a novel tree structure, called the Weighted Support FP-Tree (WSFP-Tree), that stores compressed crucial information about frequent itemsets. Empirical results show that our algorithm outperforms comparative algorithms under the windowed streaming model.

• Journal of Digital Contents Society
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• v.13 no.1
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• pp.21-29
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• 2012

In ubiquitous computing environment, rising large scale data exchange through sensor network with sharply growing the internet, the processing of the continuous stream data is required. Therefore there are some mining researches related to the extracting of frequent structures and the efficient query processing of XML stream data. In this paper, we propose a mining method to extract frequent structures of XML stream data in recent window based on the active window sliding using trigger rule. The proposed method is a basic research to control the stream data flow for data mining and continuous query by trigger rules.

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

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.55-75
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• 2010

Sequential pattern mining aims to discover interesting sequential patterns in a sequence database, and it is one of the essential data mining tasks widely used in various application fields such as Web access pattern analysis, customer purchase pattern analysis, and DNA sequence analysis. In general sequential pattern mining, only the generation order of data element in a sequence is considered, so that it can easily find simple sequential patterns, but has a limit to find more interesting sequential patterns being widely used in real world applications. One of the essential research topics to compensate the limit is a topic of weighted sequential pattern mining. In weighted sequential pattern mining, not only the generation order of data element but also its weight is considered to get more interesting sequential patterns. In recent, data has been increasingly taking the form of continuous data streams rather than finite stored data sets in various application fields, the database research community has begun focusing its attention on processing over data streams. The data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. In data stream processing, each data element should be examined at most once to analyze the data stream, and the memory usage for data stream analysis should be restricted finitely although new data elements are continuously generated in a data stream. Moreover, newly generated data elements should be processed as fast as possible to produce the up-to-date analysis result of a data stream, so that it can be instantly utilized upon request. To satisfy these requirements, data stream processing sacrifices the correctness of its analysis result by allowing some error. Considering the changes in the form of data generated in real world application fields, many researches have been actively performed to find various kinds of knowledge embedded in data streams. They mainly focus on efficient mining of frequent itemsets and sequential patterns over data streams, which have been proven to be useful in conventional data mining for a finite data set. In addition, mining algorithms have also been proposed to efficiently reflect the changes of data streams over time into their mining results. However, they have been targeting on finding naively interesting patterns such as frequent patterns and simple sequential patterns, which are found intuitively, taking no interest in mining novel interesting patterns that express the characteristics of target data streams better. Therefore, it can be a valuable research topic in the field of mining data streams to define novel interesting patterns and develop a mining method finding the novel patterns, which will be effectively used to analyze recent data streams. This paper proposes a gap-based weighting approach for a sequential pattern and amining method of weighted sequential patterns over sequence data streams via the weighting approach. A gap-based weight of a sequential pattern can be computed from the gaps of data elements in the sequential pattern without any pre-defined weight information. That is, in the approach, the gaps of data elements in each sequential pattern as well as their generation orders are used to get the weight of the sequential pattern, therefore it can help to get more interesting and useful sequential patterns. Recently most of computer application fields generate data as a form of data streams rather than a finite data set. Considering the change of data, the proposed method is mainly focus on sequence data streams.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.4
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• pp.41-60
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• 2004

A stream data is a data set that is accumulated to the data storage from a data source over time continuously. The size of this data set, in many cases. becomes increasingly large over time. To mine information from this massive data. it takes much resource such as storage, memory and time. These unique characteristics of the stream data make it difficult and expensive to use this large size data accumulated over time. Otherwise. if we use only recent or part of a whole data to mine information or pattern. there can be loss of information. which may be useful. To avoid this problem. we suggest a method that efficiently accumulates information. in the form of rule sets. over time. It takes much smaller storage compared to traditional mining methods. These accumulated rule sets are used as prediction models in the future. Based on theories of ensemble approaches. combination of many prediction models. in the form of systematically merged rule sets in this study. is better than one prediction model in performance. This study uses a customer data set that predicts buying power of customers based on their information. This study tests the performance of the suggested method with the data set alone with general prediction methods and compares performances of them.