High Utility Pattern Mining Research Articles

슬라이딩 윈도우 기반의 스트림 하이 유틸리티 패턴 마이닝 기법 성능분석

최근 무선 센서 네트워크, 사물 인터넷, 소셜 네트워크 서비스와 같은 다양한 응용 분야에서 대용량 스트림 데이터가 실시간으로 생성되고 있으며, 효율적인 기법을 통해 처리 및 분석하여 유용한 정보를 찾아내고, 이를 의사 결정을 위해 사용할 수 있도록 하는 것은 중요한 이슈 중에 하나이다. 스트림 데이터는 끊임없이 빠른 속도로 생성되므로 최소한의 접근을 통해 처리해야 하며, 신속한 저전력 처리를 필요로 하는 자원이 제한된 환경에서 분석될 수 있도록 적합한 기법이 요구된다. 이러한 문제를 해결하기 위해, 슬라이딩 윈도우 개념이 제안되어 연구되고 있다. 한편, 대용량 데이터로부터 의미 있는 정보를 찾아내기 위한 데이터 마이닝 기법 중에 하나인 패턴 마이닝은 중요 정보를 패턴 형태로 추출한다. 전통적인 빈발 패턴 마이닝은 이진 데이터베이스를 대상으로 하고 모든 아이템을 동일한 중요도로 고려함으로써 데이터 마이닝 분야에서 중요한 역할을 수행해 왔지만, 실제 데이터 특성을 반영하지 못하는 단점을 지닌다. 하이 유틸리티 패턴 마이닝은 비 이진 데이터베이스로부터 상대적인 아이템 중요도를 반영하여 더욱 의미 있는 정보를 찾아내기 위해 제안되었다. 정적 데이터를 대상으로 하는 하이 유틸리티 패턴 마이닝 기법은 그러나 스트림 데이터 처리에 적합하지 못하다. 제한된 환경에서 스트림 데이터의 특성을 반영하고 효율적으로 처리하여 중요한 정보를 찾아내기 위해 슬라이딩 윈도우 기반의 접근법이 제안되었다. 본 논문은 슬라이딩 윈도우 기반 하이 유틸리티 패턴 마이닝 기법들의 성능을 평가하고 분석하여 해당 기법들의 특성 및 발전 방향을 고찰한다. 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.

English

English

Review on Mining High Utility Patterns Decreasing Candidates

Utility mining is a latest development of data mining technology. Among utility mining issues, utility mining with the item set share structure is a hard one as no anti-monotonicity property hold swith the interestingness measure.Prior work son this problem all use a two-phase,candidate generation approach with one exception that is however in efficient and not scalable with big databases.This paper plan sa novel algorithm that find shigh utility patterns in a single phase with out generating candidate

FDHUP: Fast algorithm for mining discriminative high utility patterns

Recently, high utility pattern mining (HUPM) has been extensively studied. Many approaches for HUPM have been proposed in recent years, but most of them aim at mining HUPs without any consideration for their frequency. This has the major drawback that any combination of a low utility item with a very high utility pattern is regarded as a HUP, even if this combination has low affinity and contains items that rarely co-occur. Thus, frequency should be a key criterion to select HUPs. To address this issue, and derive high utility interesting patterns (HUIPs) with strong frequency affinity, the HUIPM algorithm was proposed. However, it recursively constructs a series of conditional trees to produce candidates and then derive the HUIPs. This procedure is time-consuming and may lead to a combinatorial explosion when the minimum utility threshold is set relatively low. In this paper, an efficient algorithm named fast algorithm for mining discriminative high utility patterns (DHUPs) with strong frequency affinity (FDHUP) is proposed to efficiently discover DHUPs by considering both the utility and frequency affinity constraints. Two compact structures named EI-table and FU-tree and three pruning strategies are introduced in the proposed algorithm to reduce the search space, and efficiently and effectively discover DHUPs. An extensive experimental study shows that the proposed FDHUP algorithm considerably outperforms the state-of-the-art HUIPM algorithm in terms of execution time, memory consumption, and scalability.

Indexed list-based high utility pattern mining with utility upper-bound reduction and pattern combination techniques

High utility pattern mining has been studied as an essential topic in the field of pattern mining in order to satisfy requirements of many real-world applications that need to process non-binary da...

Analysis of Methods for High Utility Pattern Mining without any Threshold Setting

Analysis of Methods for High Utility Pattern Mining without any Threshold Setting

Mining High Utility Patterns in One Phase without Generating Candidates

Utility mining is a new development of data mining technology. Among utility mining problems, utility mining with the itemset share framework is a hard one as no anti-monotonicity property holds with the interestingness measure. Prior works on this problem all employ a two-phase, candidate generation approach with one exception that is however inefficient and not scalable with large databases. The two-phase approach suffers from scalability issue due to the huge number of candidates. This paper proposes a novel algorithm that finds high utility patterns in a single phase without generating candidates. The novelties lie in a high utility pattern growth approach, a lookahead strategy, and a linear data structure. Concretely, our pattern growth approach is to search a reverse set enumeration tree and to prune search space by utility upper bounding. We also look ahead to identify high utility patterns without enumeration by a closure property and a singleton property. Our linear data structure enables us to compute a tight bound for powerful pruning and to directly identify high utility patterns in an efficient and scalable way, which targets the root cause with prior algorithms. Extensive experiments on sparse and dense, synthetic and real world data suggest that our algorithm is up to 1 to 3 orders of magnitude more efficient and is more scalable than the state-of-the-art algorithms.

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.

High utility pattern mining over data streams with sliding window technique

Processing changeable data streams in real time is one of the most important issues in the data mining field due to its broad applications such as retail market analysis, wireless sensor networks, and stock market prediction. In addition, it is an interesting and challenging problem to deal with the stream data since not only the data have unbounded, continuous, and high speed characteristics but also their environments have limited resources. High utility pattern mining, meanwhile, is one of the essential research topics in pattern mining to overcome major drawbacks of the traditional framework for frequent pattern mining that takes only binary databases and identical item importance into consideration. This approach conducts mining processes by reflecting characteristics of real world databases, non-binary quantities and relative importance of items. Although relevant algorithms were proposed for finding high utility patterns in stream environments, they suffer from a level-wise candidate generation-and-test and a large number of candidates by their overestimation techniques. As a result, they consume a huge amount of execution time, which is a significant performance issue since a rapid process is necessary in stream data analysis. In this paper, we propose an algorithm for mining high utility patterns from resource-limited environments through efficient processing of data streams in order to solve the problems of the overestimation-based methods. To improve mining performance with fewer candidates and search space than the previous ones, we develop two techniques for reducing overestimated utilities. Moreover, we suggest a tree-based data structure to maintain information of stream data and high utility patterns. The proposed tree is restructured by our updating method with decreased overestimation utilities to keep up-to-date stream information whenever the current window slides. Our approach also has an important effect on expert and intelligent systems in that it can provide users with more meaningful information than traditional analysis methods by reflecting the characteristics of real world non-binary databases in stream environments and emphasizing on recent data. Comprehensive experimental results show that our algorithm outperforms the existing sliding window-based one in terms of runtime efficiency and scalability.

Fast algorithm for high utility pattern mining with the sum of item quantities

In frequent pattern mining, items are considered as having the same importance in a database and their occurrence are represented as binary values in transactions. In real-world databases, however, items not only have relative importance but also are represented as non-binary values in transactions . High utility pattern mining is one of the most essential issues in the pattern mining field, which recently emerged to address the limitation of frequent pattern mining. Meanwhile, tree construction with a single database scan is significant since a database scan is a time-consuming task. In utility mining, an additional database scan is necessary to identify actual high utility patterns from candidates. In this paper, we propose a novel tree structure, namely SIQ-Tree (Sum of Item Quantities), which captures database information through a single-pass. Moreover, a restructuring method is suggested with strategies for reducing overestimated utilities. The proposed algorithm can construct the SIQ-Tree with only a single scan and decrease the number of candidate patterns effectively with the reduced overestimation utilities, through which mining performance is improved. Experimental results show that our algorithm outperforms a state-of-the-art one in terms of runtime and the number of generated candidates with a similar memory usage.

Efficient mining of high utility pattern with considering of rarity and length

Techniques for mining rare patterns have been researched in the association rule mining area because traditional frequent pattern mining methods have to generate a large amount of unnecessary patterns in order to find rare patterns from large databases. One such technique, the multiple minimum support threshold framework was devised to extract rare patterns by using a different minimum item support threshold for each item in a database. Nevertheless, this framework cannot sufficiently reflect environments of the real world. The reason is that it does not consider weights of items, such as market prices of products and fatality rates of diseases, in its mining process. Therefore, an algorithm has been proposed to mine rare patterns with utilities exceeding a user-specified minimum utility by considering rarity and utility information of items. However, since this algorithm employs the concept of traditional high utility pattern mining, patterns' lengths are not considered for determining utilities of the patterns. If the length of a pattern is sufficiently long, the pattern is more likely to have an enough utility to become a high utility pattern regardless of item utilities within the pattern. Therefore, the algorithm cannot guarantee that all items in a mined pattern have high utilities. In this paper, we propose a novel algorithm that effectively reduces such dependency of patterns on their lengths by considering their lengths in the mining process in order to mine more meaningful rare patterns compared to patterns mined by previous algorithms. Experimental results demonstrate that our algorithm extracts a lesser number of more meaningful patterns and consumes less computational resources compared to state-of-the-art algorithms.

상위 K 하이 유틸리티 패턴 마이닝 기법 성능분석

전통적인 빈발 패턴 마이닝은 데이터베이스로부터 사용자 정의 최소 임계치 이상의 빈도수를 가지는 유효 패턴들을 식별한다. 적절한 임계치 설정은 해당 도메인에 대한 사전 지식을 요구하므로 쉬운 작업이 아니다. 따라서 임계치 설정을 통한 마이닝 결과의 정밀한 제어 불가능으로 인해 도메인 지식을 기반으로 하지 않는 패턴 마이닝 방법이 필요하게 되었다. 상위 K 빈발 패턴 마이닝은 이러한 문제를 해결하기 위해 제안되었으며, 임계치 설정 없이 상위 K개의 중요 패턴들을 마이닝 한다. 사용자는 이를 적용함으로써 데이터베이스에 상관없이 가장 높은 빈도수의 패턴부터 K번째로 높은 빈도수의 패턴까지 찾아낼 수 있다. 비록 상위 K 빈발 패턴 마이닝이 임계치 설정 없이 상위 K개의 중요 패턴들을 마이닝 하지만, 트랜잭션 내 아이템 수량과 데이터베이스 내 서로 다른 아이템 중요도를 고려하지 못하여 많은 실세계 응용의 요구에 부합하지 못한다. 하이 유틸리티 패턴 마이닝은 아이템 중요도가 포함된 비 바이너리 데이터베이스의 특성을 고려하기 위해 제안되었으나 최소 임계치를 필요로 한다. 최근 임계치 설정 없는 하이 유틸리티 패턴 마이닝을 위한 상위 K 하이 유틸리티 패턴 마이닝이 개발되었으며, 이를 통해 사용자는 사전 지식 없이 원하는 수의 패턴을 마이닝 할 수 있다. 본 논문은 상위 K 하이 유틸리티 패턴 마이닝을 위한 알고리즘을 분석한다. 최신 알고리즘에 대한 성능분석을 통해 개선사항 및 발전 방향에 대해 고찰한다. Traditional frequent pattern mining discovers valid patterns with no smaller frequency than a user-defined minimum threshold from databases. In this framework, an enormous number of patterns may be extracted by a too low threshold, which makes result analysis difficult, and a too high one may generate no valid pattern. Setting an appropriate threshold is not an easy task since it requires the prior knowledge for its domain. Therefore, a pattern mining approach that is not based on the domain knowledge became needed due to inability of the framework to predict and control mining results precisely according to the given threshold. Top-k frequent pattern mining was proposed to solve the problem, and it mines top-k important patterns without any threshold setting. Through this method, users can find patterns from ones with the highest frequency to ones with the k-th highest frequency regardless of databases. In this paper, we provide knowledge both on frequent and top-k pattern mining. Although top-k frequent pattern mining extracts top-k significant patterns without the setting, it cannot consider both item quantities in transactions and relative importance of items in databases, and this is why the method cannot meet requirements of many real-world applications. That is, patterns with low frequency can be meaningful, and vice versa, in the applications. High utility pattern mining was proposed to reflect the characteristics of non-binary databases and requires a minimum threshold. Recently, top-k high utility pattern mining has been developed, through which users can mine the desired number of high utility patterns without the prior knowledge. In this paper, we analyze two algorithms related to top-k high utility pattern mining in detail. We also conduct various experiments for the algorithms on real datasets and study improvement point and development direction of top-k high utility pattern mining through performance analysis with respect to the experimental results.

Efficient algorithms for mining up-to-date high-utility patterns

High-utility pattern mining (HUPM) is an emerging topic in recent years instead of association-rule mining to discover more interesting and useful information for decision making. Many algorithms have been developed to find high-utility patterns (HUPs) from quantitative databases without considering timestamp of patterns, especially in recent intervals. A pattern may not be a HUP in an entire database but may be a HUP in recent intervals. In this paper, a new concept namely up-to-date high-utility pattern (UDHUP) is designed. It considers not only utility measure but also timestamp factor to discover the recent HUPs. The UDHUP-apriori is first proposed to mine UDHUPs in a level-wise way. Since UDHUP-apriori uses Apriori-like approach to recursively derive UDHUPs, a second UDHUP-list algorithm is then presented to efficiently discover UDHUPs based on the developed UDU-list structures and a pruning strategy without candidate generation, thus speeding up the mining process. A flexible minimum-length strategy with two specific lifetimes is also designed to find more efficient UDHUPs based on a users’ specification. Experiments are conducted to evaluate the performance of the proposed two algorithms in terms of execution time, memory consumption, and number of generated UDHUPs in several real-world and synthetic datasets.

Efficient High Utility Itemset Mining using Utility Information Record

High utility itemsets refer to the sets of items with high utility like profit in a database, and efficient mining of high utility itemsets plays an important role in many real life applications and is an important research issue in data mining area. In recent years, the problems of high utility pattern mining become one of the most important research areas in data mining. The existing high utility mining algorithm generates large number of candidate itemsets, which takes much time to find utility value of all candidate itemsets. In this paper we are implementing a data structure that stores the utility related to the item and using this data structure we are reducing time and space complexity of UP Growth and UP Growth+ Algorithms. Various Standard and synthetic datasets are used with Educational real data set. An algorithm is proposed to find set of high utility itemset which avoids the candidate itemsets generation.

Modified GUIDE (LM) algorithm for mining maximal high utility patterns from data streams

AbstractHigh utility pattern mining is an emerging research topic in the data mining field. Unlike frequent pattern mining, high utility pattern mining deals with non-binary databases, in which the information about purchased quantities of items is maintained. Due to the non-existence of anti-monotone property among the utilities of itemsets, utility mining becomes a big challenge. Moreover, discovering useful patterns from the huge number of potential patterns is a mining bottleneck. However, the compact (Closed and Maximal) high utility pattern mining moderately lessens the number of patterns, but it does not solve it. Recently, an efficient framework called GUIDE, was proposed in the literature to address this issue. Though, GUIDE effectively reduced the number of high utility patterns, yet the quality of few mined patterns and their utilities are not exact. In view of this, we propose a modified MGUIDELM algorithm to improve the quality and determine exact utilities of maximal patterns.

UP-GNIV: an expeditious high utility pattern mining algorithm for itemsets with negative utility values

Traditionally, frequent pattern mining dealt in extracting frequency pattern from transaction databases by not considering utility factors. Utility-based data mining focuses on all aspects of economic utility in data mining and is aimed at incorporating utility in both predictive and descriptive data mining tasks. High utility itemset (HUI) mining process incurs the problem of producing a large number of candidate itemsets since downward closure property used in frequent itemset mining is not applied in utility mining and itemsets associated with negative utility values are not supported by existing algorithms. Here, mining high utility itemset with negative item values using Utility Pattern-Growth approach for Negative Item Values (UP-GNIV) approach is proposed and compared against high utility itemsets with negative item values (HUINIV)-mine. The experimental result shows that the suggested approach performs well.

Top-k high utility pattern mining with effective threshold raising strategies

In pattern mining, users generally set a minimum threshold to find useful patterns from databases. As a result, patterns with higher values than the user-given threshold are discovered. However, it is hard for the users to determine an appropriate minimum threshold. The reason for this is that they cannot predict the exact number of patterns mined by the threshold and control the mining result precisely, which can lead to performance degradation. To address this issue, top-k mining has been proposed for discovering patterns from ones with the highest value to ones with the kth highest value with setting the desired number of patterns, k. Top-k utility mining has emerged to consider characteristics of real-world databases such as relative importance of items and item quantities with the advantages of top-k mining. Although a relevant algorithm has been suggested in recent years, it generates a huge number of candidate patterns, which results in an enormous amount of execution time. In this paper, we propose an efficient algorithm for mining top-k high utility patterns with highly decreased candidates. For this purpose, we develop three strategies that can reduce the search space by raising a minimum threshold effectively in the construction of a global tree, where they utilize exact and pre-evaluated utilities of itemsets. Moreover, we suggest a strategy to identify actual top-k high utility patterns from candidates with the exact and pre-calculated utilities. Comprehensive experimental results on both real and synthetic datasets show that our algorithm with the strategies outperforms state-of-the-art methods.

Incremental high utility pattern mining with static and dynamic databases

Pattern mining is a data mining technique used for discovering significant patterns and has been applied to various applications such as disease analysis in medical databases and decision making in...

Survey On: Mining High Utility Itemsets from Transactional Database

Utility mining emerges as an important topic in data mining field. Mining high utility itemsets from databases refers to finding the itemsets with high profits. The meaning of itemset utility is interestingness, importance, or profitability of an item to users. Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. Although several relevant algorithm has been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains lots of long transactions or long high utility itemsets. This research work focuses on efficient method for high utility itemset mining, to reduce number of overestimated itemsets and to reduce the search space in order to improve the performance of high utility itemset mining. Key Terms: Candidate itemset, frequent itemset, high utility itemset, utility mining, data mining. I. Introduction Now a days computers are used widely in different areas. Fast reliable and unlimited secondary storage provides a perfect environment for the users to collect and store large amount of the data. Computers are also used to extract the useful information from the mass of data. This is called as the knowledge discovery or Data mining. Data mining is the process of revealing previously unknown and potentially useful information from large databases. The primary goal is to discover the hidden patterns in the data and to extract previously unknown interesting patterns. Discovering useful patterns hidden in a database plays an essential role in several data mining tasks, like frequent pattern mining and high utility pattern mining. Among them the fundamental research topic is frequent pattern mining which can be applied to different kinds of databases such as transactional database, streaming database and time series database and many applications domains like bioinformatics, web click-stream analysis and mobile environment.

Incrementally mining high utility patterns based on pre-large concept

In traditional association rule mining, most algorithms are designed to discover frequent itemsets from a binary database. Utility mining was thus proposed to measure the utility values of purchased items for revealing high utility itemsets from a quantitative database. In the past, a two-phase high utility mining algorithm was thus proposed for efficiently discovering high utility itemsets from a quantitative database. In dynamic data mining, transactions may be inserted, deleted, or modified from a database. In this case, a batch mining procedure must rescan the whole updated database to maintain the up-to-date information. Designing an efficient approach for handling dynamic databases is thus a critical research issue in utility mining. In this paper, an incremental mining algorithm is proposed for efficiently maintaining discovered high utility itemsets based on pre-large concepts. Itemsets are first partitioned into three parts according to whether they have large (high), pre-large, or small transaction-weighted utilization in the original database and in inserted transactions. Individual procedures are then executed for each part. Experimental results show that the proposed incremental high utility mining algorithm outperforms existing algorithms.