High Average-utility Pattern Mining Research Articles

Efficient approach of high average utility pattern mining with indexed list-based structure in dynamic environments

Various studies on high utility pattern mining have been conducted to satisfy the emerging need to consider the characteristics of real-world databases, such as the importance and quantity of items. In the traditional utility-based framework, the mining result is influenced by the number of items in a pattern, or in some cases, single utilities of items. In order to overcome this drawback, high average utility pattern mining has been proposed. It provides more interesting results since it takes into account the average utility of patterns by considering their lengths. Methods based on this concept have emerged in recent years, including ones that target incremental environments. However, existing algorithms create an enormous number of candidate patterns or require complex operations during the mining process. To address this degradation, we propose a new and more efficient approach for mining high average utility patterns in dynamic environments. The proposed algorithm utilizes a data structure more efficient than previous ones, which takes the form of an indexed list. It also incorporates efficient realigning and mining techniques for handling incremental data and accurately mining results. Experimental results show the superiority of the proposed approach in terms of runtime, memory usage, scalability, and accuracy.

Mining Top-k High Average-Utility Sequential Patterns for Resource Transformation

High-utility sequential pattern mining (HUSPM) helps researchers find all subsequences that have high utility in a quantitative sequential database. The HUSPM approach appears to be well suited for resource transformation in DIKWP graphs. However, all the extensions of a high-utility sequential pattern (HUSP) also have a high utility that increases with its length. Therefore, it is difficult to obtain diverse patterns of resources. The patterns that consist of many low-utility items can also be a HUSP. In practice, such a long pattern is difficult to analyze. In addition, the low-utility items do not always reflect the interestingness of association rules. High average-utility pattern mining is considered a solution to extract more significant patterns by considering the lengths of patterns. In this paper, we formulate the problem of top-k high average-utility sequential pattern mining (HAUSPM) and propose a novel algorithm for resource transformation. We adopt a projection mechanism to improve efficiency. We also adopt the sequence average-utility-raising strategy to increase thresholds. We design the prefix extension average utility and the reduced sequence average utility by incorporating the average utility into the utility upper bounds. The results of our comparative experiments demonstrate that the proposed algorithm can achieve sufficiently good performance.

Efficient approach of sliding window-based high average-utility pattern mining with list structures

Data mining has been actively studied, and it has become more important due to the development of information technology and the demands of diverse applications, such as the retail market, medical field, and manufacturing plants. High average-utility pattern mining is a type of data mining that finds valuable patterns by dividing the utility of a pattern by the length of the pattern. It considers the length of the pattern, so it can handle the demerits of utility pattern mining and extract more important patterns. Meanwhile, stream data is produced in real time, and the importance of the recent data gradually increases as opposed to the old data. However, most of the previous high average utility pattern mining approaches cannot find the latest valuable patterns over the data streams. Although research using a sliding window model was conducted to handle only the recent data, it has a problem due to the data structure. Since the previous work is a tree-based approach, numerous candidate patterns are generated, and an additional database scan is required to calculate the actual utilities of candidates. Creating the candidate patterns reduces the efficiency of runtime and memory and makes real-time data processing difficult. We propose an efficient algorithm that uses a list structure for sliding window-based high average-utility pattern mining in this paper. The proposed algorithm scans the stream data once, mines the complete set of the recent high average-utility patterns without generating candidates, and reduces the search space by using a novel pruning technique. The experiment results show that the proposed algorithm outperforms the state-of-the-art algorithms in terms of the runtime, memory, and scalability in real and synthetic datasets.

Efficient list based mining of high average utility patterns with maximum average pruning strategies

High average utility pattern mining is the concept proposed to complement drawbacks of high utility pattern mining by considering lengths of patterns along with the utilities of the patterns. High average utility pattern mining should be able to gratify the anti-monotone property like other pattern mining techniques. Many high average utility pattern mining studies to satisfy the anti-monotone property have been proposed in order to improve various upper-bounds because the performance of pattern mining can be improved efficiently by satisfying the anti-monotone property. Although those upper-bounds can effectively reduce the search space, they still take a lot of cost to calculate all unpromising patterns or cannot find them in advance. Therefore, in this paper, a novel high average utility pattern mining approach is proposed by employing two novel upper-bounds called tight maximum average utility upper-bound and maximum remaining average utility upper-bound. Moreover, a newly suggested list-based structure, TA-List, is designed to adopt two pruning strategies. The proposed technique can efficiently extract high average utility patterns by reducing search space. To evaluate the performance of the proposed method, various experiments using real and synthetic datasets are conducted in terms of runtime, memory usage and scalability and the proposed approach is compared with the state-of-the-art high average utility pattern mining algorithms. The results of experiments show that the suggested algorithm has better performance with regard to runtime, memory usage and scalability.

One scan based high average-utility pattern mining in static and dynamic databases

High average utility pattern mining has been proposed to overcome the demerits of high utility pattern mining. Since high average utility pattern mining can extract more valuable patterns than high utility pattern mining, many related researches are being actively conducted. However, most studies in high average utility pattern mining have only focused on mining in static databases not dynamic databases. In addition, the methods of previous studies with dynamic databases consume a huge runtime and memory space due to the inefficient processes and structures. To overcome these problems, we present a novel high average utility pattern mining approach from the dynamic databases. The proposed mining approach reads a database only once and adopts a new data structure called a HAUP-List to store information of patterns more compactly. In addition, in order to reflect the incremental environments, a restructure process is designed to handle the newly inserted data. Thus, our approach can extract high average utility patterns more efficiently than the suggested methods in previous works in dynamic databases. Various experiments are conducted to demonstrate the performance of the proposed approach using both real and synthetic datasets. Results of these experiments show that the proposed mining approach outperforms the other state-of-the-art high average utility pattern mining approaches in dynamic databases.

A Data-Driven Approach for Twitter Hashtag Recommendation

This paper addresses the hashtag recommendation problem using high average-utility pattern mining. We introduce a novel framework called PM-HRec (Pattern Mining for Hashtag Recommendation). It consists of two main stages. First, offline processing transforms the corpus of tweets into a transactional database considering the temporal information of the tagged tweets (tweets with hashtags). The method discovers the temporal top k high average utility patterns . Irrelevant tagged tweets and the ontology of tagged tweets are also constructed offline. Second, an online processing inputs the utility patterns, the ontology, and the irrelevant tagged tweets to extract the most relevant hashtags for a given orpheline tweet (tweet without hashtags). Extensive experiments were carried out on large tweets collections. The proposed PM-HRec outperforms the existing state of the art hashtag recommendation approaches in terms of quality of recommended hashtags and runtime processing.

Efficiently updating the discovered high average-utility itemsets with transaction insertion

High-utility itemset mining (HUIM) is an extension of frequent-itemset mining (FIM) but considers the unit profit and quantity of items to discover the set of high-utility itemsets (HUIs). Traditionally, the utility of an itemset is the summation of the utilities of the itemset in all the transactions regardless of its length. This approach is, however, inappropriate in real-world applications since the utility of the itemset increases along with the number of items within it. High average-utility itemset mining (HAUIM) was designed to provide more reasonable utility measure by taking the size of the itemset into account. Existing algorithms can only handle, however, the static database and unsuitable for the dynamic environment since the size of data is frequently changed in real-life situations. In this paper, an incremental high-average utility pattern mining (IHAUPM) algorithm is presented to handle the incremental database with transaction insertion. The well-known fast updated (FUP) concept in the FIM is modified to adopt the designed algorithm, thus efficiently updating the discovered HAUIs. Based on the designed model for HAUIM with transaction insertion, the proposed IHAUPM algorithm can easily only handle the inserted transactions. Experiments are carried on six datasets and the results showed that the designed algorithm has better performance than the state-of-the-art algorithms performing in the batch manner.

Damped window based high average utility pattern mining over data streams

Data mining methods have been required in both commercial and non-commercial areas. In such circumstances, pattern mining techniques can be used to find meaningful pattern information. Utility pattern mining (UPM) is more suitable for evaluating the usefulness of patterns. The method introduced in this paper employs the high average utility pattern mining (HAUPM) approach, which is one of the UPM approaches and discovers interesting patterns of which the items have more meaningful relations among one another by using a novel utility measure. Meanwhile, past research on pattern mining algorithms mainly focus on mining tasks processing static database such as batch operations. Most continuous, unbounded stream data such as data constantly produced from heart beat sensors should be treated differently with respect to importance because up-to-date data may have higher influence than old data. Therefore, our approach also adopts the concept of the damped window model to gain more useful patterns in stream environments. Various experiments are performed on real datasets in order to demonstrate that the designed method not only provides important, recent pattern information but also requires less computational resources such as execution time, memory usage, scalability and significant test.

Mining recent high average utility patterns based on sliding window from stream data

Utility pattern mining is a technique that finds valuable patterns from large-sized databases with each item’s importance and quantity information associated with it. The representative utility pattern mining technique, high utility pattern mining (HUPM), calculates the utilities of patterns by sum mating all of the item utilities in the patterns. However, such utility measures for patterns in HUPM have a drawback in whichpatterns with long lengths tend to have utilities sufficient to become high utility patterns. For these reasons, high average utility pattern mining (HAUPM) employing different utility measures has been studied in order to consider such pattern length factors. Recently, techniques for handling stream data are necessary because many data sources, e.g. sensors and POS devices, produce data in real time. However, all the existing HAUPM algorithms are unable to find up-to-date, meaningful patterns over data streams. We thus propose the first sliding window based HAUPM algorithm discovering recent high average utility patterns over data streams. Based on the sliding window model, our algorithm divides stream data into numerous batches, and keeps only recent batches in its window. Thereby, the algorithm can mine recent, important patterns over data streams. We also introduce a new strategy that enhances the performance of our algorithm by minimizing the overestimated average utilities stored in the proposed data structure. The experimental results show that our algorithm outperforms the competitors.