Mining lossless closed frequent patterns with weight constraints

Frequent pattern mining is one of main concerns in data mining tasks. In frequent pattern mining, closed frequent pattern mining and weighted frequent pattern mining are two main approaches to reduce the search space. Although many related studies have been suggested, no mining algorithm considers both paradigms. Even if closed frequent pattern mining represents exactly the same knowledge and weighted frequent pattern mining provides a way to discover more important patterns, the incorporation of closed frequent pattern mining and weight frequent pattern mining may loss information. Based on our analysis of joining orders, we propose closed weighted frequent pattern mining, and present how to discover succinct but lossless closed frequent pattern with weight constraints. To our knowledge, ours is the first work specifically to consider both constraints. An extensive performance study shows that our algorithm outperforms previous algorithms. In addition, it is efficient and scalable.     

频繁模式挖掘的约束算法

在频繁模式挖掘过程中能够动态改变约束的算法比较少.提出了一种基于约束的频繁模式挖掘算法MCFP.MCFP首先按照约束的性质来建立频繁模式树,并且只需扫描一遍数据库,然后建立每个项的条件树,挖掘以该项为前缀的最大频繁模式,并用最大模式树来存储,最后根据最大模式来找出所有支持度明确的频繁模式.MCFP算法允许用户在挖掘频繁模式过程中动态地改变约束.实验表明,该算法与iCFP算法相比是很有效的.     

Spatiotemporal feature mining algorithm based on multiple minimum supports of pattern growth in Internet of Things

The temporal and spatial characteristics of users are involved in most Internet of Things (IoT) applications. The spatial and temporal movement patterns of users are the most direct manifestation of the temporal and spatial characteristics. The user’s interests, activities, experience and other characteristics are reflected by mobile mode. In view of the low clustering efficiency of moving objects in convergent pattern mining in the IoT, a spatiotemporal feature mining algorithm based on multiple minimum supports of pattern growth is proposed. Based on the temporal characteristics of user trajectories, frequent and asynchronous periodic spatiotemporal movement patterns are mined. Firstly, the location sequence is modeled, and the time information is added to the model. Then, a mining algorithm of asynchronous periodic sequential pattern is adopted. The algorithm is based on multiple minimum supports of pattern growth. According to multiple minimum supports, the sequential pattern of asynchronous period is mined deeply and recursively. Finally, the proposed method is validated and evaluated by Gowalla dataset, in which the user characteristics are truly reflected. It is shown by the experimental results that the average pointwise mutual information (PWI) of the proposed algorithm reaches 0.93. And the algorithm is proved to be effective and accurate.

     

一种基于频繁模式树的约束最大频繁项目集挖掘及其更新算法

目前已提出了许多快速的关联规则挖掘算法,实际上用户只关心部分关联规则,如他们仅想知道包含指定项目的规则．当这些约束被用于数据预处理或将它结合到数据挖掘算法中去时,可以显著减少算法的执行时间．为此,考虑了一类包含或不包含某些项目的布尔表达式约束条件,提出了一种快速的基于FP—tree的约束最大频繁项目集挖掘算法CMFIMA,并对其更新问题进行了研究,提出了一种增量式更新约束最大频繁项目集挖掘算法CMFIUA．     

动态空间网络中的黑洞模式挖掘算法

黑洞模式是人类移动模式研究中的标志性成果,但在移动模式的演化建模方面存在局限性,因此研究具有时间演化特性的黑洞模式。新模式定义需要满足群体规模性、空间区域性和时间持续性3方面要求。提出具有时间演化特性的动态空间网络模型,基于此模型定义新的黑洞模式,并提出相应的挖掘算法。为了提升模式挖掘算法的效率,设计了基于时空划分的候选模式剪枝算法,有效降低了挖掘算法在时空维中的搜索代价。最后,基于真实数据的实验结果表明了该黑洞模式及其挖掘算法的有效性和可行性。     

一种改进的FP-Growth算法及其在业务关联中的应用

基于FP-树的FP-Growth算法在挖掘频繁模式过程中需要递归地产生大量的条件FP-树,效率不高,并且不太适合应用在移动通信业务交叉销售等具有业务约束的关联规则挖掘中。因此,提出了基于项目约束的频繁模式树ICFP-树和直接在此树上进行挖掘的新算法——ICFP-Mine。理论分析和实验结果表明,ICFP-Mine算法在内存占用和时间开销等方面比FP-Growth算法更优越,在移动通信业务交叉销售领域的应用中取得了较好的效果。     

带有间隔约束的多序列模式挖掘

研究这样一个问题：给定多序列、支持度阈值和间隔约束,从多序列中挖掘所有出现次数不小于支持度阈值的频繁序列模式,这里要求模式中任意两个相邻元素在序列中的出现都要满足用户自定义的间隔约束,并且模式在序列中的出现要满足one-off条件。在解决该问题上,已有算法M-OneOffMine在计算模式的支持度时,只考虑模式的每个字符在序列中的首次出现,导致计算的模式支持度远小于其真实支持度,以致许多频繁的模式没有被挖掘出来。为此,设计了一个有效的带有间隔约束的多序列模式挖掘算法--MMSP算法：首先,通过采用二维表保存模式的候选位置;然后,根据候选位置采用最左最优的思想选择匹配位置。通过生物DNA序列进行实验,多序列中元素序列数目不变而序列长度变化时,MMSP挖掘出的频繁模式总数是同类算法M-OneOffMine的3.23倍;在元素序列个数变化时,MMSP挖掘出的频繁模式个数平均是M-OneOffMine的4.11倍;这两种情况下MMSP都有更好的时间性能。在模式长度变化时,MMSP挖掘出的频繁模式个数分别平均是M-OneOffMine的2.21倍和MPP的5.24倍。同时还验证了M-OneOffMine挖掘到的模式是MMSP挖掘到的频繁的子集。实验结果表明,MMSP算法不仅可以挖掘到更多的频繁模式,而且时间花费更少,更适合于实际的应用。     

Discovering Transitional Patterns and Their Significant Milestones in Transaction Databases

A transaction database usually consists of a set of time-stamped transactions. Mining frequent patterns in transaction databases has been studied extensively in data mining research. However, most of the existing frequent pattern mining algorithms (such as Apriori and FP-growth) do not consider the time stamps associated with the transactions. In this paper, we extend the existing frequent pattern mining framework to take into account the time stamp of each transaction and discover patterns whose frequency dramatically changes over time. We define a new type of patterns, called transitional patterns, to capture the dynamic behavior of frequent patterns in a transaction database. Transitional patterns include both positive and negative transitional patterns. Their frequencies increase/decrease dramatically at some time points of a transaction database. We introduce the concept of significant milestones for a transitional pattern, which are time points at which the frequency of the pattern changes most significantly. Moreover, we develop an algorithm to mine from a transaction database the set of transitional patterns along with their significant milestones. Our experimental studies on real-world databases illustrate that mining positive and negative transitional patterns is highly promising as a practical and useful approach for discovering novel and interesting knowledge from large databases.     

轨迹数据的时空模式挖掘与管理决策研究综述

时空轨迹数据的获取变得越来越容易,轨迹数据刻画了移动对象的行为模式与活动规律,是对移动对象在时空环境下的移动模式和行为特征的真实写照,在城市规划、交通管理、服务推荐、位置预测等领域具有重要的应用价值。这些过程通常需要通过对时空轨迹数据进行模式挖掘才能得以实现。简述了轨迹数据挖掘的预处理和基本步骤,归纳了异常轨迹检测方法的分类,分析、总结了近年来基于轨迹数据的四种模式挖掘,从管理决策角度对轨迹数据挖掘进行相关综述和分析,有望为轨迹数据的模式挖掘与管理决策提供必要的文献资料和理论基础。     

快速多层次关联规则的挖掘

知识发现是指对原始数据进行分析，提取出隐含的，有用的规则，是当前快速发展的研究领域，是知识获取的重要方法，关联规则是知识发现的重要研究内容之一，本文提出了一种新的多层次关联规则挖掘算法ＭＬ＿ＡＲ，算法ＭＬ＿ＡＲ在挖掘过程中，只对最低概括层次上的候选系模式进行模式的匹配计算，求解出简化的频繁式集合，最后再求解各个概括层次上的繁频模式集合，算法ＭＬ＿ＡＲ有效地利用了概括的层次关系，减少了模式的匹配计算     

Efficient mining of sequential patterns with time constraints by delimited pattern growth

An active research topic in data mining is the discovery of sequential patterns, which finds all frequent subsequences in a sequence database. The generalized sequential pattern (GSP) algorithm was proposed to solve the mining of sequential patterns with time constraints, such as time gaps and sliding time windows. Recent studies indicate that the pattern-growth methodology could speed up sequence mining. However, the capabilities to mine sequential patterns with time constraints were previously available only within the Apriori framework. Therefore, we propose the DELISP (delimited sequential pattern) approach to provide the capabilities within the pattern-growth methodology. DELISP features in reducing the size of projected databases by bounded and windowed projection techniques. Bounded projection keeps only time-gap valid subsequences and windowed projection saves nonredundant subsequences satisfying the sliding time-window constraint. Furthermore, the delimited growth technique directly generates constraint-satisfactory patterns and speeds up the pattern growing process. The comprehensive experiments conducted show that DELISP has good scalability and outperforms the well-known GSP algorithm in the discovery of sequential patterns with time constraints.     

Developing data allocation schemes by incremental mining of user moving patterns in a mobile computing system

In this paper, we present a new data mining algorithm which involves incremental mining for user moving patterns in a mobile computing environment and exploit the mining results to develop data allocation schemes so as to improve the overall performance of a mobile system. First, we propose an algorithm to capture the frequent user moving patterns from a set of log data in a mobile environment. The algorithm proposed is enhanced with the incremental mining capability and is able to discover new moving patterns efficiently without compromising the quality of results obtained. Then, in light of mining results of user moving patterns and the properties of data objects, we develop data allocation schemes that can utilize the knowledge of user moving patterns for proper allocation of both personal and shared data. By employing the data allocation schemes, the occurrences of costly remote accesses can be minimized and the performance of a mobile computing system is thus improved. For personal data allocation, two schemes are devised: one utilizes the set level of moving patterns and the other utilizes their path level. Schemes for shared data are also developed. Performance of these schemes is comparatively analyzed.     

Demand-driven frequent itemset mining using pattern structures

Frequent itemset mining aims at discovering patterns the supports of which are beyond a given threshold. In many applications, including network event management systems, which motivated this work, patterns are composed of items each described by a subset of attributes of a relational table. As it involves an exponential mining space, the efficient implementation of user preferences and mining constraints becomes the first priority for a mining algorithm. User preferences and mining constraints are often expressed using patterns attribute structures. Unlike traditional methods that mine all frequent patterns indiscriminately, we regard frequent itemset mining as a two-step process: the mining of the pattern structures and the mining of patterns within each pattern structure. In this paper, we present a novel architecture that uses pattern structures to organize the mining space. In comparison with the previous techniques, the advantage of our approach is two-fold: (i) by exploiting the interrelationships among pattern structures, execution times for mining can be reduced significantly; and (ii) more importantly, it enables us to incorporate high-level simple user preferences and mining constraints into the mining process efficiently. These advantages are demonstrated by our experiments using both synthetic and real-life datasets.     

Mining sequential patterns by pattern-growth: the PrefixSpan approach

Sequential pattern mining is an important data mining problem with broad applications. However, it is also a difficult problem since the mining may have to generate or examine a combinatorially explosive number of intermediate subsequences. Most of the previously developed sequential pattern mining methods, such as GSP, explore a candidate generation-and-test approach [R. Agrawal et al. (1994)] to reduce the number of candidates to be examined. However, this approach may not be efficient in mining large sequence databases having numerous patterns and/or long patterns. In this paper, we propose a projection-based, sequential pattern-growth approach for efficient mining of sequential patterns. In this approach, a sequence database is recursively projected into a set of smaller projected databases, and sequential patterns are grown in each projected database by exploring only locally frequent fragments. Based on an initial study of the pattern growth-based sequential pattern mining, FreeSpan [J. Han et al. (2000)], we propose a more efficient method, called PSP, which offers ordered growth and reduced projected databases. To further improve the performance, a pseudoprojection technique is developed in PrefixSpan. A comprehensive performance study shows that PrefixSpan, in most cases, outperforms the a priori-based algorithm GSP, FreeSpan, and SPADE [M. Zaki, (2001)] (a sequential pattern mining algorithm that adopts vertical data format), and PrefixSpan integrated with pseudoprojection is the fastest among all the tested algorithms. Furthermore, this mining methodology can be extended to mining sequential patterns with user-specified constraints. The high promise of the pattern-growth approach may lead to its further extension toward efficient mining of other kinds of frequent patterns, such as frequent substructures.     

Mining top-k frequent patterns with combination reducing techniques

Top-k frequent pattern mining finds interesting patterns from the highest support to the k-th support. The approach can be effectively applied in numerous fields such as marketing, finance, bio-data analysis, and so on since it does not need constraints by a minimum support threshold. Top-k mining methods use the support of the k-th pattern, not a user-specified minimum support. Thus, the methods conduct mining operations based on very low supports until the k-th pattern is detected. When a low support is used in the mining process, single-paths with numerous items are generated, where the top-k mining algorithm extracts valid patterns by combining the items for each single-path. Therefore, the bigger the number of combinations is, the larger the increase in time and memory consumption is. In this paper, in order to mine top-k frequent patterns more efficiently, we consider converting patterns obtained from single-paths into composite patterns during the mining process and recovering them as the original patterns when the top-k frequent patterns are extracted. For this, we define a new concept, the composite pattern, and propose novel techniques for reducing pattern combinations in the single-path. Two algorithms are introduced in this paper, where the former is CRM (Combination Reducing method), applying our reduction manner, and the latter is CRMN (Combination Reducing method for N-itemset), considering N-itemset, i.e., patterns’ lengths. A performance evaluation shows that CRM and CRMN algorithms can efficiently reduce pattern combinations in single-paths compared to state-of-the-art algorithms. The experimental results also illustrate that our approaches have outstanding performance in terms of runtime, memory, and scalability.     

基于环境标记约束的不确定轨迹频繁路径挖掘

针对环境约束的不确定轨迹数据的频繁路径问题,设计了一种适应于严格时间约束条件下基于环境约束的位置不确定的移动概率序列挖掘算法（UETFP-PrefixSpan）,算法通过设置类标号把不同环境下的不确定轨迹数据区分开,利用概率支持度对频繁项集进行了重新定义,通过减少某些特定序列模式生成过程的扫描,来减少投影数据库的规模及扫描投影数据库的时间,提高算法效率。测试实验结果表明,改进后的UETFP-PrefixSpan算法挖掘结果更符合现实情况,算法执行效率更高。     

带通配符和One-Off条件的序列模式挖掘

很多应用领域产生大量的序列数据。如何从这些序列数据中挖掘具有重要价值的模式,已成为序列模式挖掘研究的主要任务。研究这样一个问题：给定序列S、支持度阈值和间隔约束,从序列S中挖掘所有出现次数不小于给定支持度阈值的频繁序列模式,并且要求模式中任意两个相邻元素在序列中的出现位置满足用户定义的间隔约束。设计了一种有效的带有通配符的模式挖掘算法One-Off Mining,模式在序列中的出现满足One-Off条件,即模式的任意两次出现都不共享序列中同一位置的字符。在生物DNA序列上的实验结果表明,One-Off Mining比相关的序列模式挖掘算法具有更好的时间性能和完备性。     

Tree pattern mining with tree automata constraints

Most work on pattern mining focuses on simple data structures such as itemsets and sequences of itemsets. However, a lot of recent applications dealing with complex data like chemical compounds, protein structures, XML and Web log databases and social networks, require much more sophisticated data structures such as trees and graphs. In these contexts, interesting patterns involve not only frequent object values (labels) appearing in the graphs (or trees) but also frequent specific topologies found in these structures. Recently, several techniques for tree and graph mining have been proposed in the literature. In this paper, we focus on constraint-based tree pattern mining. We propose to use tree automata as a mechanism to specify user constraints over tree patterns. We present the algorithm CoBMiner which allows user constraints specified by a tree automata to be incorporated in the mining process. An extensive set of experiments executed over synthetic and real data (XML documents and Web usage logs) allows us to conclude that incorporating constraints during the mining process is far more effective than filtering the interesting patterns after the mining process.     

Mining constrained inter-sequence patterns: a novel approach to cope with item constraints

Data mining has become increasingly important in the Internet era. The problem of mining inter-sequence pattern is a sub-task in data mining with several algorithms in the recent years. However, these algorithms only focus on the transitional problem of mining frequent inter-sequence patterns and most frequent inter-sequence patterns are either redundant or insignificant. As such, it can confuse end users during decision-making and can require too much system resources. This led to the problem of mining inter-sequence patterns with item constraints, which addressed the problem when end-users only concerned the patterns contained a number of specific items. In this paper, we propose two novel algorithms for it. First is the ISP-IC (Inter-Sequence Pattern with Item Constraint mining) algorithm based on a theorem that quickly determines whether an inter-sequence pattern satisfies the constraints. Then, we propose a way to improve the strategy of ISP-IC, which is then applied to the \(i\)ISP-IC algorithm to enhance the performance of the process. Finally, pi ISP-IC, a parallel version of \(i\)ISP-IC, will be presented. Experimental results show that pi ISP-IC algorithm outperforms the post-processing of the-state-of-the-art method for mining inter-sequence patterns (EISP-Miner), ISP-IC, and \(i\)ISP-IC algorithms in most of the cases.     

一种直接在Trans-树中挖掘频繁模式的新算法

Frequent pattern mining plays an essential role in many important data mining tasks. FP-growth is a very efficient algorithm for frequent pattern mining. However, it still suffers from creating conditional FP-tree separately and recursively during the mining process. In this paper, we propose a new algorithm, called Least-Item-First Pat-tern Growth (LIFPG), for mining frequent patterns. LIFPG mines frequent patterns directly in Trans-tree withoutusing any additional data structures. The key idea is that least items are always considered first when the current pat-tern growth. By this way, conditional sub-tree can be created directly in Trans-tree by adjusting node-links and re-counting counts of some nodes. Experiments show that, in comparison with FP-Growth, our algorithm is about fourtimes faster and saves half of memory;it also has good time and space scalability with the number of transactions,and has an excellent performance in dense dataset mining as well.