挖掘数据流界标窗口Top-K频繁项集

数据流频繁项集挖掘是目前数据挖掘与知识发现领域的热点研究课题,在许多领域有重要应用.然而支持度阈值的设定需要一定的领域知识,设置不当会给后续的分析处理带来很多困难和不必要的负担,因此挖掘数据流top-K频繁项集有重要意义.提出一个挖掘数据流界标窗口top-K频繁项集的动态增量近似算法TOPSIL-Miner,为此设计了存储流数据摘要信息的概要结构TOPSIL-Tree以及动态记录挖掘相关信息的树层最大支持度表MaxSL、项目序表OIL,TOPSET和最小支持度表MinSL等,并分析了与这些概要结构相关的挖掘特性.在此基础上研究算法的3种优化措施:1)剪枝当前数据流的平凡项集;2)挖掘过程中启发式自适应提升挖掘阈值;3)动态提升剪枝阈值.对算法的误差上界进行了分析研究.最后通过实验验证了算法的可行性、精确性和时空高效性.     

Interactive mining of top-K frequent closed itemsets from data streams

Mining closed frequent itemsets from data streams is of interest recently. However, it is not easy for users to determine a proper minimum support threshold. Hence, it is more reasonable to ask users to set a bound on the result size. Therefore, an interactive single-pass algorithm, called TKC-DS (top-K frequent closed itemsets of data streams), is proposed for mining top-K closed itemsets from data streams efficiently. A novel data structure, called CIL (closed itemset lattice), is developed for maintaining the essential information of closed itemsets generated so far. Experimental results show that the proposed TKC-DS algorithm is an efficient method for mining top-K frequent itemsets from data streams.     

基于向量的数据流滑动窗口中最大频繁项集挖掘*

针对相关算法在挖掘数据流最大频繁项集时所存在的问题,提出了一种基于向量的数据流滑动窗口中最大频繁项集挖掘算法。该算法首先用向量作为概要数据结构,采用定量更新滑动窗口策略解决时间粒度问题;其次通过位运算产生频繁项集,利用矩阵和数组存储辅助信息,深度优先搜索产生最大频繁项集时利用剪枝策略进一步减少挖掘时间;最后用索引链表存储挖掘结果以提高超集检测效率。理论分析和实验结果验证了该算法的有效性。     

An efficient algorithm for mining top-<Emphasis Type="Italic">k</Emphasis> on-shelf high utility itemsets

High on-shelf utility itemset (HOU) mining is an emerging data mining task which consists of discovering sets of items generating a high profit in transaction databases. The task of HOU mining is more difficult than traditional high utility itemset (HUI) mining, because it also considers the shelf time of items, and items having negative unit profits. HOU mining can be used to discover more useful and interesting patterns in real-life applications than traditional HUI mining. Several algorithms have been proposed for this task. However, a major drawback of these algorithms is that it is difficult for users to find a suitable value for the minimum utility threshold parameter. If the threshold is set too high, not enough patterns are found. And if the threshold is set too low, too many patterns will be found and the algorithm may use an excessive amount of time and memory. To address this issue, we propose to address the problem of top-k on-shelf high utility itemset mining, where the user directly specifies k, the desired number of patterns to be output instead of specifying a minimum utility threshold value. An efficient algorithm named KOSHU (fast top-K on-shelf high utility itemset miner) is proposed to mine the top-k HOUs efficiently, while considering on-shelf time periods of items, and items having positive and/or negative unit profits. KOSHU introduces three novel strategies, named efficient estimated co-occurrence maximum period rate pruning, period utility pruning and concurrence existing of a pair 2-itemset pruning to reduce the search space. KOSHU also incorporates several novel optimizations and a faster method for constructing utility-lists. An extensive performance study on real-life and synthetic datasets shows that the proposed algorithm is efficient both in terms of runtime and memory consumption and has excellent scalability.     

Mining top-k maximal reference sequences from streaming web click-sequences with a damped sliding window

Online mining of path traversal patterns from continuous Web click streams is one of the challenging research problems of Web usage mining. Most of previous works focus on mining path traversal patterns over the entire history of Web click streams. Mining the recent changes of Web click streams can provide valuable information for the analysis of the Web click streams. In this paper, we propose a new, online mining algorithm, called Top-DSW (top-k path traversal patterns of stream Damped Sliding Window), to discover the set of top-k path traversal patterns from streaming maximal forward references, where k is the desired number of path traversal patterns to be mined. An effective summary data structure, called TKP-DSW-list (a list of top-k path traversal patterns of stream Damped Sliding Windows) is developed to maintain the essential information about the top-k path traversal patterns from the maximal forward references within a stream damped sliding window. An effective space pruning mechanism, called TKR-list-maintain, is developed to control the memory requirement of the TKP-DSW-list. Experimental studies show that the proposed Top-DSW algorithm is an efficient, single-pass algorithm for online mining of the set of top-k path traversal patterns over stream damped sliding windows.     

Mining top-k regular-frequent itemsets using database partitioning and support estimation

Temporal regularity of itemset appearance can be regarded as an important criterion for measuring the interestingness of itemsets in several applications. A frequent itemset can be said to be regular-frequent in a database if it appears at a regular period. Therefore, the problem of mining a complete set of regular-frequent itemsets requires the specification of a support and a regularity threshold. However, in practice, it is often difficult for users to provide an appropriate support threshold. In addition, the use of a support threshold tends to produce a large number of regular-frequent itemsets and it might be better to ask for the number of desired results. We thus propose an efficient algorithm for mining top-k regular-frequent itemsets without setting a support threshold. Based on database partitioning and support estimation techniques, the proposed algorithm also uses a best-first search strategy with only one database scan. We then compare our algorithm with the state-of-the-art algorithms for mining top-k regular-frequent itemsets. Our experimental studies on both synthetic and real data show that our proposal achieves high performance for small and large values of k.     

一种基于滑动窗口的数据流频繁闭项集挖掘算法

频繁项集挖掘是数据流挖掘中的一个热点问题.提出了一种新的数据流频繁闭项集挖掘算法MFCI-SW.首先设计了两个新的数据结构:频繁闭项集表FCIL和频繁闭合模式树MFCI-SW-Tree,在此基础上以滑动窗口中的基本窗口为更新单位,在每个基本窗口中提取出频繁闭项集的数据项,将其支持度F和窗口序列号K存到FCIL中;然后随着新基本窗口的到来,通过删除频繁闭项集表中K值最小的数据项和插入新数据项完成对FCIL的更新和MFCI-SW-Tree树的裁剪;最后在MFCI-SW-Tree中可以迅速挖掘出满足用户需要的频繁闭项集.实验结果证明了该算法在执行效率上明显优于DS-CFI算法.     

数据流最大频繁项挖掘方法

提出基于事务矩阵挖掘最大频繁项集的方法AFMI,该方法采取迭代精简事务矩阵的方式求解所有事务中的最大频繁项集,从精简后的事务向量交集的子集中搜索最大频繁项集,并运用逻辑运算和剪枝方法提高挖掘效率。基于AFMI方法,研究挖掘滑动窗口数据流最大频繁项集算法AFMI+,该算法可使用户周期性地挖掘当前窗口中的最大频繁项集。实验结果表明,AFMI和AFMI+算法均具有较好的性能。     

TopPI: An efficient algorithm for item-centric mining

In this paper, we introduce item-centric mining, a new semantics for mining long-tailed datasets. Our algorithm, TopPI, finds for each item its top-k most frequent closed itemsets. While most mining algorithms focus on the globally most frequent itemsets, TopPI guarantees that each item is represented in the results, regardless of its frequency in the database.TopPI allows users to efficiently explore Web data, answering questions such as “what are the k most common sets of songs downloaded together with the ones of my favorite artist?”. When processing retail data consisting of 55 million supermarket receipts, TopPI finds the itemset “milk, puff pastry” that appears 10,315 times, but also “frangipane, puff pastry” and “nori seaweed, wasabi, sushi rice” that occur only 1120 and 163 times, respectively. Our experiments with analysts from the marketing department of our retail partner demonstrate that item-centric mining discover valuable itemsets. We also show that TopPI can serve as a building-block to approximate complex itemset ranking measures such as the p-value.Thanks to efficient enumeration and pruning strategies, TopPI avoids the search space explosion induced by mining low support itemsets. We show how TopPI can be parallelized on multi-cores and distributed on Hadoop clusters. Our experiments on datasets with different characteristics show the superiority of TopPI when compared to standard top-k solutions, and to Parallel FP-Growth, its closest competitor.     

基于矩阵的数据流Top-k频繁项集挖掘算法

传统的数据挖掘算法在挖掘频繁项集时会产生大量的冗余项集,影响挖掘效率。为此,提出一种基于矩阵的数据流Top-k频繁项集挖掘算法。引入2个0-1矩阵,即事务矩阵和二项集矩阵。采用事务矩阵表示滑动窗口模型中的事务列表,通过计算每行的支持度得到二项集矩阵。利用二项集矩阵得到候选项集,将事务矩阵中对应的行做逻辑与运算,计算出候选项集的支持度,从而得到Top-k频繁项集。把挖掘的结果存入数据字典中,当用户查询时,能够按支持度降序输出Top-k频繁项集。实验结果表明,该算法在挖掘过程中能避免冗余项集的产生,在保证正确率的前提下具有较高的时间效率。     

数据流中一种基于滑动窗口的前K个

数据流频繁项集挖掘是当今数据挖掘和知识学习领域重要的研究课题之一。数据流高速性、连续性、无界性、实时性对挖掘算法在时间和空间方面提出了更高的要求。传统的数据挖掘算法由于其存储结构需要频繁地维护,其挖掘方式的精度和速度较低,空间、时间效率不高。在基于粒计算和ECLAT算法的基础上提出一种挖掘数据流滑动窗口中topK频繁项集算法,采用二进制方式存储项,利用位移运算实现增量更新,实施与运算计算项集支持度,同时利用二分查找法插入到项目序表中,输出前K个频繁项。实验结果表明,该算法在K取值不太高时具有较好的时空高     

Mining top-<Emphasis Type="Italic">K</Emphasis> frequent itemsets through progressive sampling

We study the use of sampling for efficiently mining the top-K frequent itemsets of cardinality at most w. To this purpose, we define an approximation to the top-K frequent itemsets to be a family of itemsets which includes (resp., excludes) all very frequent (resp., very infrequent) itemsets, together with an estimate of these itemsets’ frequencies with a bounded error. Our first result is an upper bound on the sample size which guarantees that the top-K frequent itemsets mined from a random sample of that size approximate the actual top-K frequent itemsets, with probability larger than a specified value. We show that the upper bound is asymptotically tight when w is constant. Our main algorithmic contribution is a progressive sampling approach, combined with suitable stopping conditions, which on appropriate inputs is able to extract approximate top-K frequent itemsets from samples whose sizes are smaller than the general upper bound. In order to test the stopping conditions, this approach maintains the frequency of all itemsets encountered, which is practical only for small w. However, we show how this problem can be mitigated by using a variation of Bloom filters. A number of experiments conducted on both synthetic and real benchmark datasets show that using samples substantially smaller than the original dataset (i.e., of size defined by the upper bound or reached through the progressive sampling approach) enable to approximate the actual top-K frequent itemsets with accuracy much higher than what analytically proved.     

An efficient algorithm for mining temporal high utility itemsets from data streams

Utility of an itemset is considered as the value of this itemset, and utility mining aims at identifying the itemsets with high utilities. The temporal high utility itemsets are the itemsets whose support is larger than a pre-specified threshold in current time window of the data stream. Discovery of temporal high utility itemsets is an important process for mining interesting patterns like association rules from data streams. In this paper, we propose a novel method, namely THUI (Temporal High Utility Itemsets)-Mine, for mining temporal high utility itemsets from data streams efficiently and effectively. To the best of our knowledge, this is the first work on mining temporal high utility itemsets from data streams. The novel contribution of THUI-Mine is that it can effectively identify the temporal high utility itemsets by generating fewer candidate itemsets such that the execution time can be reduced substantially in mining all high utility itemsets in data streams. In this way, the process of discovering all temporal high utility itemsets under all time windows of data streams can be achieved effectively with less memory space and execution time. This meets the critical requirements on time and space efficiency for mining data streams. Through experimental evaluation, THUI-Mine is shown to significantly outperform other existing methods like Two-Phase algorithm under various experimental conditions.     

带间隔约束的Top-k对比序列模式挖掘

对比序列模式能够表达序列数据集合间的差异,在商品推荐、用户行为分析和电力供应预测等领域有广泛的应用.已有的对比序列模式挖掘算法需要用户设定正例支持度阈值和负例支持度阈值.在不具备足够先验知识的情况下,用户难以设定恰当的支持度阈值,从而可能错失一些对比显著的模式.为此,提出了带间隔约束的top-k对比序列模式挖掘算法kDSP-Miner(top-k distinguishing sequential patterns with gap constraint miner).kDSP-Miner中用户只需设置期望发现的对比最显著的模式个数,从而避免了直接设置对比支持度阈值.相应地,挖掘算法更容易使用,并且结果更易于解释.同时,为了提高算法执行效率,设计了若干剪枝策略和启发策略.进一步设计了kDSP-Miner的多线程版本,以提高其对高维序列元素情况的处理能力.通过在真实世界数据集上的详实实验,验证了算法的有效性和执行效率.     

Mining top?k frequent patterns without minimum support threshold

Finding frequent patterns play an important role in mining association rules, sequences, episodes, Web log mining and many other interesting relationships among data. Frequent pattern mining methods often produce a huge number of frequent itemsets that is not feasible for effective usage. The number of highly correlated patterns is usually very small and may even be one. Most of the existing frequent pattern mining techniques often require the setting of many input parameters and may involve multiple passes over the database. Minimum support is the widely used parameter in frequent pattern mining to discover statistically significant patterns. Specifying appropriate minimum support is a challenging task for a data analyst as the choice of minimum support value is somewhat arbitrary. Generally, it is required to repeatedly execute an algorithm, heuristically tuning the value of minimum support over a wide range, until the desired result is obtained, certainly, a very time-consuming process. Setting up an inappropriate minimum support may also cause an algorithm to fail in finding the true patterns. We present a novel method to efficiently retrieve top few maximal frequent patterns in order of significance without use of the minimum support parameter. Instead, we are only required to specify a more human understandable parameter, namely the desired number itemsets k. Our technique requires only a single pass over the database and generation of length two itemsets. The association ratio graph is proposed as a compact structure containing concise information, which is created in time quadratic to the size of the database. Algorithms are described for using this graph structure to discover top-most and top-k maximal frequent itemsets without minimum support threshold. To effectively achieve this, the method employs construction of an all path source-to-destination tree to discover all maximal cycles in the graph. The results can be ranked in decreasing order of significance. Results are presented demonstrating the performance advantages to be gained from the use of this approach.     

一种无阈值的频繁模式生成算法

在数据挖掘的关联规则挖掘算法中,传统的频繁模式挖掘算法需要用户指定项集的最小支持度。引入Top-k模式挖掘概念的改进算法虽然无需指定最小支持度,但仍需指定阈值k。针对上述问题,对传统挖掘算法进行改进,提出一种新的频繁模式挖掘算法(TNFP- growth)。该算法无需指定最小支持度或阈值,按照支持度降序排列进行模式挖掘,有序地返回频繁模式给用户。实验结果证明,该算法的执行效率更高,具有更强的伸缩性。     

Top-k high average-utility itemsets mining with effective pruning strategies

High average-utility itemset (HAUI) mining has recently received interest in the data mining field due to its balanced utility measurement, which considers not only profits and quantities of items but also the lengths of itemsets. Although several algorithms have been designed for the task of HAUI mining in recent years, it is hard for users to determine an appropriate minimum average-utility threshold for the algorithms to work efficiently and control the mining result precisely. In this paper, we address this issue by introducing a framework of top-k HAUI mining, where \(k\) is the desired number of high average-utility itemsets to be mined instead of setting a minimum average-utility threshold. An efficient list based algorithm named TKAU is proposed to mine the top-k high average-utility itemsets in a single phase. TKAU introduces two novel strategies, named EMUP and EA to avoid performing costly join operations for calculating the utilities of itemsets. Moreover, three strategies named RIU, CAD, and EPBF are also incorporated to raise its internal minimal average-utility threshold effectively, and thus reduce the search space. Extensive experiments on both real and synthetic datasets show that the proposed algorithm has excellent performance and scalability.     

Mining top-k frequent patterns over data streams sliding window

Frequent pattern mining in data streams is an important research topic in the data mining community. In previous studies, a minimum support threshold was assumed to be available for mining frequent patterns. However, setting such a threshold is typically difficult. Hence, it is more reasonable to ask users to set a bound on the result size. The present study considers mining top-k frequent patterns from data streams using a sliding window technique. A single-pass algorithm, called MSWTP, is developed for the generation of top-k frequent patterns without a threshold. In the method, the content of the transactions in the sliding window is incrementally maintained in a summary data structure, named SWTP-tree, by scanning the stream only once. To make the mining operation efficient, insignificant patterns are distinguished from others by applying the Chernoff bound. Two kinds of obsolete pattern and one kind of insignificant pattern are periodically pruned from the pattern tree. Whenever necessary, the k most frequent patterns can be selected from SWTP-tree in order of their descending frequency. The performance of the proposed technique is evaluated via simulation experiments. The results show that the proposed method is both efficient and scalable, and that it outperforms comparable algorithms.     

基于散列的频繁项集分组算法

Apriori算法是频繁项集挖掘的经典算法。针对Apriori算法的剪枝操作和多次扫描数据集的缺点,提出了基于散列的频繁项集分组(HFG)算法。证明了2-项集剪枝性质,采用散列技术存储频繁2-项集,将Apriori算法剪枝操作的时间复杂度从O(k×|Lk|)降低到O(1);定义了首项的子项集概念,将数据集划分为以Ii为首项的数据子集并采用分组索引表存储,在求以Ii为首项的频繁项集时,只扫描以Ii为首项的数据子集,减少了对数据集扫描的时间代价。实验结果表明,由于HFG算法的剪枝操作产生了累积效益,以及分组扫描排除了无效的项集和元组,使得HFG算法在时间性能方面与Apriori算法相比有较大提高。     

微阵列数据中Top-k频繁闭合项集挖掘

现有大部分微阵列数据中频繁闭合项集的挖掘需要事先给定最小支持度,但在实际应用中该最小支持度很难确定。针对该问题,提出top-k频繁闭合项集挖掘算法,基于自顶向下宽度优先搜索策略挖掘项集长度不小于min_l的top-k频繁闭合项集,并对搜索空间进行有效修剪,从而提高搜索速度。实验结果表明,该算法的时间性能在多数情况下优于CARPENTER算法。