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.     

数据流频繁模式挖掘综述

一些先进应用如欺诈检测和趋势学习等带来了数据流频繁模式挖掘的发展。不同于静态数据,数据流挖掘面临着时空约束和项集组合爆炸等问题。对已有数据流频繁模式挖掘算法进行综述并对经典和最新算法进行分析。按照模式集合的完整程度进行分类,数据流中频繁模式分为全集模式和压缩模式。压缩模式主要包括闭合模式、最大模式、top-k模式以及三者的组合模式。不同之处是闭合模式是无损压缩的,而其他模式是有损压缩的。为了得到有趣的频繁模式,可以挖掘基于用户约束的模式。为了处理数据流中的新近事务,将算法分为基于窗口模型和基于衰减模型的方法。数据流中模式挖掘常见的还包含序列模式和高效用模式,对经典和最新算法进行介绍。最后给出了数据流模式挖掘的下一步工作。     

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.     

A general streaming algorithm for pattern discovery

Discovering frequent patterns over event sequences is an important data mining problem. Existing methods typically require multiple passes over the data, rendering them unsuitable for streaming contexts. We present the first streaming algorithm for mining frequent patterns over a window of recent events in the stream. We derive approximation guarantees for our algorithm in terms of: (i) the separation of frequent patterns from the infrequent ones, and (ii) the rate of change of stream characteristics. Our parameterization of the problem provides a new sweet spot in the tradeoff between making distributional assumptions over the stream and algorithmic efficiencies of mining. We illustrate how this yields significant benefits when mining practical streams from neuroscience and telecommunications logs.     

基于triemerging机制数据流滑动窗口模型的频繁模式挖掘

因树型结构的良好表达能力,在互联网中传输的信息流越来越多以树型结构形式存储。但由于流式数据的时效性,隐含在数据流中的知识会随着时间的推移发生改变。针对数据流场景下挖掘最近时间段内的频繁子树模式的问题,提出了一种滑动窗口模型下挖掘频繁子树模式算法——SWMiner算法,用于挖掘数据流下任意时刻窗口下所有的频繁子树模式。SWMiner算法使用基于前缀树的结构来压缩存储生成的树模式,并且使用trie merging机制有效地更新子树模式的支持度。实验结果表明,SWMiner算法在滑动窗口模型中的性能优于目前现有的常用算法,能有效地挖掘最近时间段内的频繁树模式。     

Interactive mining of high utility patterns over data streams

High utility pattern (HUP) mining over data streams has become a challenging research issue in data mining. When a data stream flows through, the old information may not be interesting in the current time period. Therefore, incremental HUP mining is necessary over data streams. Even though some methods have been proposed to discover recent HUPs by using a sliding window, they suffer from the level-wise candidate generation-and-test problem. Hence, they need a large amount of execution time and memory. Moreover, their data structures are not suitable for interactive mining. To solve these problems of the existing algorithms, in this paper, we propose a novel tree structure, called HUS-tree (high utility stream tree) and a new algorithm, called HUPMS (high utility pattern mining over stream data) for incremental and interactive HUP mining over data streams with a sliding window. By capturing the important information of stream data into an HUS-tree, our HUPMS algorithm can mine all the HUPs in the current window with a pattern growth approach. Furthermore, HUS-tree is very efficient for interactive mining. Extensive performance analyses show that our algorithm is very efficient for incremental and interactive HUP mining over data streams and significantly outperforms the existing sliding window-based HUP mining algorithms.     

Towards a variable size sliding window model for frequent itemset mining over data streams

Sliding window is a widely used model for data stream mining due to its emphasis on recent data and its bounded memory requirement. The main idea behind a transactional sliding window is to keep a fixed size window over a data stream. The window size is kept constant by removing old transactions from the window, when new transactions arrive. Older transactions of window are removed irrespective to whether a significant change has occurred or not. Another challenge of sliding window model is determining window size. The classic approach for determining the window size is to obtain it from the user. In order to determine the precise size of the window, the user must have prior knowledge about the time and scale of changes within the data stream. However, due to the unpredictable changing nature of data streams, this prior knowledge cannot be easily determined. Moreover, by using a fixed window size during a data stream mining, the performance of this model is degraded in terms of reflecting recent changes. Based on these observations, this study relaxes the notion of window size and proposes a new algorithm named VSW (Variable Size sliding Window frequent itemset mining) which is suitable for observing recent changes in the set of frequent itemsets over data streams. The window size is determined dynamically based on amounts of concept change that occurs within the arriving data stream. The window expands as the concept becomes stable and shrinks when a concept change occurs. In this study, it is shown that if stale transactions are removed from the window after a concept change, updated frequent itemsets always belong to the most recent concept. Experimental evaluations on both synthetic and real data show that our algorithm effectively detects the concept change, adjust the window size, and adapts itself to the new concepts along the data stream.     

Approximate mining of maximal frequent itemsets in data streams with different window models

A data stream is a massive, open-ended sequence of data elements continuously generated at a rapid rate. Mining data streams is more difficult than mining static databases because the huge, high-speed and continuous characteristics of streaming data. In this paper, we propose a new one-pass algorithm called DSM-MFI (stands for Data Stream Mining for Maximal Frequent Itemsets), which mines the set of all maximal frequent itemsets in landmark windows over data streams. A new summary data structure called summary frequent itemset forest (abbreviated as SFI-forest) is developed for incremental maintaining the essential information about maximal frequent itemsets embedded in the stream so far. Theoretical analysis and experimental studies show that the proposed algorithm is efficient and scalable for mining the set of all maximal frequent itemsets over the entire history of the data streams.     

基于时间衰减模型的数据流频繁模式挖掘

频繁模式挖掘是数据流挖掘中的重要研究课题. 针对数据流的时效性和流中心的偏移性特点, 提出了界标窗口模型与时间衰减模型相结合的数据流频繁模式挖掘算法. 该算法通过动态构建全局模式树, 利用时间指数衰减函数对模式树中各模式的支持数进行统计, 以此刻画界标窗口内模式的频繁程度; 进而, 为有效降低空间开销, 设计了剪枝阈值函数, 用于对预期难以成长为频繁的模式及时从全局树中剪除. 本文对出现在算法中的重要参数和阈值进行了深入分析. 一系列实验表明, 与现有同类算法MSW相比, 该算法挖掘精度高(平均超过90%), 内存开销小, 速度上可以满足高速数据流的处理要求, 且可以适应不同事务数量、不同事务平均长度和不同最大潜在频繁模式平均长度的数据流频繁模式挖掘.     

TWCT-Stream:数据流上的频繁模式挖掘算法

提出一种结合倾斜时间窗的TWCT树结构,可以保存不同时间粒度下频繁模式的完全集,并设计了其顺序更新和删除算法,使其能够存储在外存,从而有效地降低算法的内存空间需求。结合TWCT树结构特点,提出了数据流上的频繁模式挖掘算法TWCT-Stream,其模式生长的TWCT-Growth算法按字典顺序生成频繁模式,以配合TWCT结构的顺序更新。实验证实算法的内存需求低于FP-Stream等同类算法。     

Sliding window based weighted maximal frequent pattern mining over data streams

As data have been accumulated more quickly in recent years, corresponding databases have also become huger, and thus, general frequent pattern mining methods have been faced with limitations that do not appropriately respond to the massive data. To overcome this problem, data mining researchers have studied methods which can conduct more efficient and immediate mining tasks by scanning databases only once. Thereafter, the sliding window model, which can perform mining operations focusing on recently accumulated parts over data streams, was proposed, and a variety of mining approaches related to this have been suggested. However, it is hard to mine all of the frequent patterns in the data stream environment since generated patterns are remarkably increased as data streams are continuously extended. Thus, methods for efficiently compressing generated patterns are needed in order to solve that problem. In addition, since not only support conditions but also weight constraints expressing items’ importance are one of the important factors in the pattern mining, we need to consider them in mining process. Motivated by these issues, we propose a novel algorithm, weighted maximal frequent pattern mining over data streams based on sliding window model (WMFP-SW) to obtain weighted maximal frequent patterns reflecting recent information over data streams. Performance experiments report that MWFP-SW outperforms previous algorithms in terms of runtime, memory usage, and scalability.     

挖掘数据流任意滑动时间窗口内频繁模式

由于数据流的流动性与连续性,数据流所蕴含的知识会随着时间的推移而发生变化.因此,在绝大多数数据流的应用中,用户往往对新产生的流数据所包含的知识要比对历史流数据所包含的知识感兴趣得多.提出了一种挖掘数据流任意大小滑动时间窗口内频繁模式的方法MSW(mining sliding window).当数据流流过时,该方法使用滑动窗口树SW-tree在单遍扫描流数据的条件下及时捕获数据流上最新的模式信息.同时,该方法还周期性地删除滑动窗口树上过期的及不频繁的模式分支,从而降低滑动窗口树的空间复杂度与维护代价.此外,该方法还应用时间衰减模型逐步降低历史事务模式支持数的权重,并由此来区分最近产生事务与历史事务的模式.大量仿真实验的结果表明,算法MSS具有较高的效率与优良的可扩展性,同时也优于其他同类算法.     

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.     

概念漂移数据流挖掘算法综述

数据流是一种新型的数据模型,具有动态、无限、高维、有序、高速和变化等特性。在真实的数据流环境中,一些数据分布是随着时间改变的,即具有概念漂移特征,称为可变数据流或概念漂移数据流。因此处理数据流模型的方法需要处理时空约束和自适应调整概念变化。对概念漂移问题和概念漂移数据流分类、聚类和模式挖掘等内容进行综述。首先介绍概念漂移的类型和常用概念改变检测方法。为了解决概念漂移问题,数据流挖掘中常使用滑动窗口模型对新近事务进行处理。数据流分类常用的模型包括单分类模型和集成分类模型,常用的方法包括决策树、分类关联规则等。数据流聚类方式通常包括基于k- means的和非基于k- means的。模式挖掘可以为分类、聚类和关联规则等提供有用信息。概念漂移数据流中的模式包括频繁模式、序列模式、episode、模式树、模式图和高效用模式等。最后详细介绍其中的频繁模式挖掘算法和高效用模式挖掘算法。     

挖掘数据流滑动时间窗口内Top-K频繁模式

由于数据流滑动时间窗口中流数据包含模式的支持度是动态变化的,很难给出一个合适的支持度门限来挖掘数据流滑动时间窗口内的频繁模式.在研究数据流滑动时间窗口内流数据变化特点的基础上,论文提出了一种挖掘数据流滑动时间窗口内Top-k频繁模式的方法,该方法能够在保证模式挖掘误差基础上快速删除窗口内不频繁模式信息,保留重要的模式信息,并能按照支持度降序输出Top-k频繁模式.仿真实验结果表明,该算法具有较好的效率和正确性,并优于其它同类算法.     

基于最右扩展枚举的半结构化数据最大模式挖掘方法研究

本文以标记有序树作为半结构化数据的数据模型 ,研究了半结构化数据的树状最大频繁模式挖掘问题 .已有挖掘算法通常挖掘所有频繁模式 ,其中很多模式为其它模式的子模式 ,针对该问题 ,设计实现了一种最大模式挖掘算法 .该算法采用最右扩展枚举方法无重复枚举所有候选模式 ,利用频繁模式扩展森林实现高效剪枝扩展和挖掘频繁叶模式 ,通过计算频繁叶模式间的包含关系挖掘树状最大频繁模式 .试验结果表明该算法具有良好性能     

数据流高效用模式挖掘综述

数据流高效用模式挖掘方法是以二进制的频繁模式挖掘方法为前提,引入项的内部效用和外部效用,在模式挖掘过程中可以考虑项的重要性,从而挖掘更有价值的模式。从关键窗口技术、常用方法、表示形式等角度对数据流高效用模式挖掘方法进行分析并总结其相关算法,从而研究其特点、优势、劣势以及其关键问题所在。具体来说,说明了数据流高效用模式常用的概念;对处理数据流高效用模式的关键窗口技术进行了分析,涉及到滑动、衰减、界标和倾斜窗口模型;研究了一阶段和两阶段的数据流高效用模式挖掘方法;分析了高效用模式的表示形式,即完全高效用模式和压缩高效用模式;介绍了其他的数据流高效用模式,包括序列高效用模式、混合高效用模式以及高平均效用模式等;最后展望了数据流高效用模式挖掘的进一步研究方向。     

减少候选项集的数据流高效用项集挖掘算法

大数据环境下高效用项集挖掘算法中过多的候选项集极大地降低了算法的时空效率,提出了一种减少候选项集的数据流高效用项集挖掘算法。首先,通过数据流中当前窗口的一次扫描建立一个全局树,并降低全局树中头表入口与节点的冗余效用值;然后,基于全局树生成候选模式,基于增长算法降低局部树的候选项集效用;最终,从候选模式中选出高效用模式。基于真实数据流的实验结果表明,本算法的时空效率与内存占用比均优于其他数据流的高效用模式挖掘算法。     

窗口模式下在线数据流中频繁项集的挖掘*

拟采用一种基于滑动窗模式的单遍挖掘算法,专注于处理近期数据;为了减少处理时间和占用的内存,设计了一种新的事务表示方法。通过处理这个事务的表达式,频繁项集可以被高效输出,并解决了使用基于Apriori理论的算法时,由候选频繁1-项集生成频繁2-项集时数据项顺序判断不准确问题。该算法称为MRFI-SW算法。     

基于概率衰减窗口模型的不确定数据流频繁模式挖掘

考虑到不确定数据流的不确定性,设计了一种新的概率频繁模式树PFP-tree和基于该树的概率频繁模式挖掘方法PFP-growth.PFP-growth使用事务性不确定数据流及概率衰减窗口模型,通过计算各概率数据项的期望支持度以发现概率频繁模式,其主要特点有:考虑到窗口内不同时间到达数据项的贡献度不同,采用概率衰减窗口模型计算期望支持度,以提高模式挖掘准确度;设置数据项索引表和事务索引表,以加快频繁模式树检索速度;通过剪枝删除不可能成为频繁模式的结点,以降低模式树的存储及检索开销;对每个结点都设立一个事务概率信息链表,以支持数据项在不同事务中具有不同概率的情形.实验结果表明,PFP-growth在保证挖掘模式准确度的前提下,在处理时间和内存空间等方面都具有较好的性能.