• 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.

• Journal of KIISE:Databases
• /
• v.35 no.3
• /
• pp.199-207
• /
• 2008

The FP-tree(Frequency Pattern Tree) mining association rules algorithm was proposed to improve mining performance by reducing DB scan overhead dramatically, and it is recognized that the performance of it is better than that of any other algorithms based on different approaches. But the FP-tree algorithm needs a few more memory because it has to store all transactions including frequent itemsets of the DB. This paper implements a FP-tree algorithm on a general purpose UNK system and compares it with the DHP(Direct Hashing and Pruning) algorithm which uses hash tree and direct hash table from the point of memory usage and execution time. The results show surprisingly that the FP-tree algorithm is poor than the DHP algorithm in some cases even if the system memory is sufficient for the FP-tree. The characteristics of the test data are as follows. The site of DB is look, the number of total items is $1K{\sim}7K$, avenrage length of transactions is $5{\sim}10$, avergage size of maximal frequent itemsets is $2{\sim}12$(these are typical attributes of data for large-scale convenience stores).

• The KIPS Transactions:PartD
• /
• v.16D no.2
• /
• pp.237-248
• /
• 2009

Recently, based on dynamic location or mobility of moving object, many researches on pattern mining methods actively progress to extract more available patterns from various moving patterns for development of location based services. The performance of moving pattern mining depend on how analyze and process the huge set of spatio-temporal data. Some of traditional spatio-temporal pattern mining methods[1-6,8-11]have proposed to solve these problem, but they did not solve properly to reduce mining execution time and minimize required memory space. Therefore, in this paper, we propose new spatio-temporal pattern mining method which extract the sequential and periodic frequent moving patterns efficiently from the huge set of spatio-temporal moving data. The proposed method reduces mining execution time of $83%{\sim}93%$ rate on frequent moving patterns mining using the moving sequence tree which generated from historical data of moving objects based on hash tree. And also, for minimizing the required memory space, it generalize the detained historical data including spatio-temporal attributes into the real world scope of space and time using spatio-temporal concept hierarchy.

• The KIPS Transactions:PartD
• /
• v.17D no.4
• /
• pp.253-258
• /
• 2010

Traditional frequent pattern mining considers equal profit/weight value of every item. Weighted Frequent Pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery by considering different weights for different items. Existing algorithms in this area are based on fixed weight. But in our real world scenarios the price/weight/importance of a pattern may vary frequently due to some unavoidable situations. Tracking these dynamic changes is very necessary in different application area such as retail market basket data analysis and web click stream management. In this paper, we propose a novel concept of dynamic weight and an algorithm DWFPM (dynamic weighted frequent pattern mining). Our algorithm can handle the situation where price/weight of a pattern may vary dynamically. It scans the database exactly once and also eligible for real time data processing. To our knowledge, this is the first research work to mine weighted frequent patterns using dynamic weights. Extensive performance analyses show that our algorithm is very efficient and scalable for WFP mining using dynamic weights.

• Journal of Internet Computing and Services
• /
• v.10 no.5
• /
• pp.49-63
• /
• 2009

The performance of spatio-temporal moving pattern mining depends on how to analyze and process the huge set of spatio-temporal data due to the nature of it. The several method was presented in order to solve the problems in which existing spatio-temporal moving pattern mining methods[1-10] have, such as increasing execution time and required memory size during the pattern mining, but they did not solve properly yet. Thus, we proposed the STMP/MST method[11] as a preceding research in order to extract effectively sequential and/or periodical frequent occurrence moving patterns from the huge set of spatio-temporal moving data. The proposed method reduces patterns mining execution time, using the moving sequence tree based on hash tree. And also, to minimize the required memory space, it generalizes detailed historical data including spatio-temporal attributes into the real world scopes of space and time by using spatio-temporal concept hierarchy. In this paper, in order to verify the effectiveness of the STMP/MST method, we compared and analyzed performance with existing spatio-temporal moving pattern mining methods based on the quantity of mining data and minimum support factor.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.8
• /
• pp.1998-2004
• /
• 2009

Recently, due to technical developments of various storage devices and networks, the amount of data increases rapidly. The large volume of data streams poses unique space and time constraints on the data mining process. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Most of the researches based on the support are concerned with the frequent itemsets, but ignore the infrequent itemsets even if it is crucial. In this paper, we propose an efficient method WSFI-Mine(Weighted Support Frequent Itemsets Mine) to mine all frequent itemsets by one scan from the data stream. This method can discover the closed frequent itemsets using DCT(Data Stream Closed Pattern Tree). We compare the performance of our algorithm with DSM-FI and THUI-Mine, under different minimum supports. As results show that WSFI-Mine not only run significant faster, but also consume less memory.

• Journal of Information Science Theory and Practice
• /
• v.2 no.2
• /
• pp.33-47
• /
• 2014

Mobile devices are the most important equipment for accessing various kinds of services. These services are accessed using wireless signals, the same used for mobile calls. Today mobile services provide a fast and excellent way to access all kinds of information via mobile phones. Mobile service providers are interested to know the access behavior pattern of the users from different locations at different timings. In this paper, we have introduced an associated tree for analyzing user behavior patterns while moving from one location to another. We have used four different parameters, namely user, location, dwell time, and services. These parameters provide stronger frequent accessing patterns by matching joins. These generated patterns are valuable for improving web services, recommending new services, and predicting useful services for individuals or groups of users. In addition, an experimental evaluation has been conducted on simulated data. Finally, performance of the proposed approach has been measured in terms of efficiency and scalability. The proposed approach produces excellent results.

• The KIPS Transactions:PartD
• /
• v.15D no.6
• /
• pp.741-754
• /
• 2008

Due to the characteristics of a data stream, it is very important to confine the memory usage of a data mining process regardless of the amount of information generated in the data stream. For this purpose, this paper proposes the Prime pattern tree(PPT) for finding frequent itemsets over data streams with using the confined memory space. Unlike a prefix tree, a node of a PPT can maintain the information necessary to estimate the current supports of several itemsets together. The length of items in a prime pattern can be reduced the total number of nodes and controlled by split_delta $S_{\delta}$. The size and the accuracy of the PPT is determined by $S_{\delta}$. The accuracy is better as the value of $S_{\delta}$ is smaller since the value of $S_{\delta}$ is large, many itemsets are estimated their frequencies. So it is important to consider trade-off between the size of a PPT and the accuracy of the mining result. Based on this characteristic, the size and the accuracy of the PPT can be flexibly controlled by merging or splitting nodes in a mining process. For finding all frequent itemsets over the data stream, this paper proposes a PPT to replace the role of a prefix tree in the estDec method which was proposed as a previous work. It is efficient to optimize the memory usage for finding frequent itemsets over a data stream in confined memory space. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.

• International Journal of Contents
• /
• v.3 no.2
• /
• pp.18-24
• /
• 2007

Biological sequences such as DNA and amino acid sequences typically contain a large number of items. They have contiguous sequences that ordinarily consist of more than hundreds of frequent items. In biological sequences analysis(BSA), a frequent contiguous sequence search is one of the most important operations. Many studies have been done for mining sequential patterns efficiently. Most of the existing methods for mining sequential patterns are based on the Apriori algorithm. In particular, the prefixSpan algorithm is one of the most efficient sequential pattern mining schemes based on the Apriori algorithm. However, since the algorithm expands the sequential patterns from frequent patterns with length-1, it is not suitable for biological datasets with long frequent contiguous sequences. In recent years, the MacosVSpan algorithm was proposed based on the idea of the prefixSpan algorithm to significantly reduce its recursive process. However, the algorithm is still inefficient for mining frequent contiguous sequences from long biological data sequences. In this paper, we propose an efficient method to mine maximal frequent contiguous sequences in large biological data sequences by constructing the spanning tree with a fixed length. To verify the superiority of the proposed method, we perform experiments in various environments. The experiments show that the proposed method is much more efficient than MacosVSpan in terms of retrieval performance.

• Journal of KIISE:Databases
• /
• v.36 no.5
• /
• pp.341-351
• /
• 2009

Recently high utility pattern (HUP) mining is one of the most important research issuer in data mining since it can consider the different weight Haloes of items. However, existing mining algorithms suffer from the performance degradation because it cannot easily apply Apriori-principle for pattern mining. In this paper, we introduce new high utility pattern mining approach by using a prefix-tree as in FP-Growth algorithm. Our approach stores the weight value of each item into a node and utilizes them for pruning unnecessary patterns. We compare the performance characteristics of three different prefix-tree structures. By thorough experimentation, we also prove that our approach can give performance improvement to a degree.