SMCA: a general model for mining asynchronous periodic patterns in temporal databases

Mining periodic patterns in time series databases is an important data mining problem with many applications. Previous studies have considered synchronous periodic patterns where misaligned occurrences are not allowed. However, asynchronous periodic pattern mining has received less attention and only been discussed for a sequence of symbols where each time point contains one event. In this paper, we propose a more general model of asynchronous periodic patterns from a sequence of symbol sets where a time slot can contain multiple events. Three parameters min/spl I.bar/rep, max/spl I.bar/dis, and global/spl I.bar/rep are employed to specify the minimum number of repetitions required for a valid segment of nondisrupted pattern occurrences, the maximum allowed disturbance between two successive valid segments, and the total repetitions required for a valid sequence. A 4-phase algorithm is devised to discover periodic patterns from a time series database presented in vertical format. The experiments demonstrate good performance and scalability with large frequent patterns.     

New and efficient knowledge discovery of partial periodic patterns with multiple minimum supports

The problem of mining partial periodic patterns is an important issue with many applications. Previous studies to find these patterns encounter efficiency and effectiveness problem. The efficiency problem is that most previous methods were proposed to find frequent partial periodic patterns by extending the well-known Apriori-like algorithm. However, these methods generate many candidate partial periodic patterns to calculate the patterns’ supports, spending much time for discovering patterns. The effective problem is that only one minimum support threshold is set to find frequent partial periodic patterns but the results is not practical for real-world. In real-life circumstances, some rare or specific events may occur with lower frequencies but their occurrences may offer some vital information to be referred in decision making. If the minimum support is set too high, the associations between events along with higher and lower frequencies cannot be evaluated so that significant knowledge will be ignored. In this study, an algorithm to overcome these two problems has been proposed to generating redundant candidate patterns and setting only one minimum support threshold. The algorithm greatly improves the efficiency and effectiveness. First, it eliminates the need to generate numerous candidate partial periodic patterns thus reducing database scanning. Second, the minimum support threshold of each event can be specified based in its real-life occurring frequency.     

Effective periodic pattern mining in time series databases

The goal of analyzing a time series database is to find whether and how frequent a periodic pattern is repeated within the series. Periodic pattern mining is the problem that regards temporal regularity. However, most of the existing algorithms have a major limitation in mining interesting patterns of users interest, that is, they can mine patterns of specific length with all the events sequentially one after another in exact positions within this pattern. Though there are certain scenarios where a pattern can be flexible, that is, it may be interesting and can be mined by neglecting any number of unimportant events in between important events with variable length of the pattern. Moreover, existing algorithms can detect only specific type of periodicity in various time series databases and require the interaction from user to determine periodicity. In this paper, we have proposed an algorithm for the periodic pattern mining in time series databases which does not rely on the user for the period value or period type of the pattern and can detect all types of periodic patterns at the same time, indeed these flexibilities are missing in existing algorithms. The proposed algorithm facilitates the user to generate different kinds of patterns by skipping intermediate events in a time series database and find out the periodicity of the patterns within the database. It is an improvement over the generating pattern using suffix tree, because suffix tree based algorithms have weakness in this particular area of pattern generation. Comparing with the existing algorithms, the proposed algorithm improves generating different kinds of interesting patterns and detects whether the generated pattern is periodic or not. We have tested the performance of our algorithm on both synthetic and real life data from different domains and found a large number of interesting event sequences which were missing in existing algorithms and the proposed algorithm was efficient enough in generating and detecting periodicity of flexible patterns on both types of data.     

一种基于MDL的日志序列模式挖掘算法

日志数据是互联网系统产生的过程性事件记录数据,从日志数据中挖掘出高质量序列模式可帮助工程师高效开展系统运维工作。针对传统模式挖掘算法结果冗余的问题,提出一种从时序日志序列中挖掘序列模式（DTS）的算法。DTS采用启发式思路挖掘能充分代表原序列中事件关系和时序规律的模式集合,并将最小描述长度准则应用于模式挖掘,设计一种考虑事件关系和时序关系的编码方案,以解决模式规模爆炸问题。在真实日志数据集上的实验结果表明,与SQS、CSC与ISM等序列模式挖掘算法相比,该算法能高效挖掘出含义丰富且冗余度低的序列模式。     

时空轨迹序列模式挖掘方法综述

在全球定位、移动通信技术迅速发展的背景下涌现出了海量的时空轨迹数据,这些数据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,蕴含了丰富的信息,这些信息对于城市规划、交通管理、服务推荐、位置预测等领域具有重要的应用价值,而时空轨迹数据在这些领域的应用通常需要通过对时空轨迹数据进行序列模式挖掘才能得以实现。时空轨迹序列模式挖掘旨在从时空轨迹数据集中找出频繁出现的序列模式,例如： 位置模式（频繁轨迹、热点区域）、活动周期模式、语义行为模式,从而挖掘时空数据中隐藏的信息。总结近年来时空轨迹序列模式挖掘的研究进展,先介绍时空轨迹序列的数据特点及应用,再描述时空轨迹模式的挖掘过程：从基于时空轨迹序列来挖掘位置模式、周期模式、语义模式这三个方面来介绍该领域的研究情况,最后阐述现有时空轨迹序列模式挖掘方法存在的问题,并展望时空轨迹序列模式挖掘方法未来的发展趋势。     

Similarity-Profiled Temporal Association Mining

Given a time stamped transaction database and a user-defined reference sequence of interest over time, similarity-profiled temporal association mining discovers all associated item sets whose prevalence variations over time are similar to the reference sequence. The similar temporal association patterns can reveal interesting relationships of data items which co-occur with a particular event over time. Most works in temporal association mining have focused on capturing special temporal regulation patterns such as cyclic patterns and calendar scheme-based patterns. However, our model is flexible in representing interesting temporal patterns using a user-defined reference sequence. The dissimilarity degree of the sequence of support values of an item set to the reference sequence is used to capture how well its temporal prevalence variation matches the reference pattern. By exploiting interesting properties such as an envelope of support time sequence and a lower bounding distance for early pruning candidate item sets, we develop an algorithm for effectively mining similarity-profiled temporal association patterns. We prove the algorithm is correct and complete in the mining results and provide the computational analysis. Experimental results on real data as well as synthetic data show that the proposed algorithm is more efficient than a sequential method using a traditional support-pruning scheme.     

Incremental, online, and merge mining of partial periodic patterns in time-series databases

Mining of periodic patterns in time-series databases is an interesting data mining problem. It can be envisioned as a tool for forecasting and prediction of the future behavior of time-series data. Incremental mining refers to the issue of maintaining the discovered patterns over time in the presence of more items being added into the database. Because of the mostly append only nature of updating time-series data, incremental mining would be very effective and efficient. Several algorithms for incremental mining of partial periodic patterns in time-series databases are proposed and are analyzed empirically. The new algorithms allow for online adaptation of the thresholds in order to produce interactive mining of partial periodic patterns. The storage overhead of the incremental online mining algorithms is analyzed. Results show that the storage overhead for storing the intermediate data structures pays off as the incremental online mining of partial periodic patterns proves to be significantly more efficient than the nonincremental nononline versions. Moreover, a new problem, termed merge mining, is introduced as a generalization of incremental mining. Merge mining can be defined as merging the discovered patterns of two or more databases that are mined independently of each other. An algorithm for merge mining of partial periodic patterns in time-series databases is proposed and analyzed.     

Discovering injective episodes with general partial orders

Frequent episode discovery is a popular framework for temporal pattern discovery in event streams. An episode is a partially ordered set of nodes with each node associated with an event type. Currently algorithms exist for episode discovery only when the associated partial order is total order (serial episode) or trivial (parallel episode). In this paper, we propose efficient algorithms for discovering frequent episodes with unrestricted partial orders when the associated event-types are unique. These algorithms can be easily specialized to discover only serial or parallel episodes. Also, the algorithms are flexible enough to be specialized for mining in the space of certain interesting subclasses of partial orders. We point out that frequency alone is not a sufficient measure of interestingness in the context of partial order mining. We propose a new interestingness measure for episodes with unrestricted partial orders which, when used along with frequency, results in an efficient scheme of data mining. Simulations are presented to demonstrate the effectiveness of our algorithms.     

A parallel algorithm for mining multiple partial periodic patterns

The mining of partial periodic patterns is an interesting type of data mining that is widely used in the analysis of markets, such as for stock management and sales management. However, the existence of huge data sets make the scalability of data-mining algorithms a very important objective, and in recent years parallel computing has been applied to general data-mining algorithms. This paper addresses the problem of mining multiple partial periodic patterns in a parallel computing environment. To reduce the cost of communication between the processors, our approach employs the independence property of prime numbers to classify partial periodic patterns into multiple independent sets. Moreover, a novel method of distributing mining tasks among the processors is proposed. A set of simulations is used to demonstrate the benefits of our approach.     

A PSO-AB classifier for solving sequence classification problems

Recently, considerable attention has focused on compound sequence classification methods which integrate multiple data mining techniques. Among these methods, sequential pattern mining (SPM) based sequence classifiers are considered to be efficient for solving complex sequence classification problems. Although previous studies have demonstrated the strength of SPM-based sequence classification methods, the challenges of pattern redundancy, inappropriate sequence similarity measures, and hard-to-classify sequences remain unsolved. This paper proposes an efficient two-stage SPM-based sequence classification method to address these three problems. In the first stage, during the sequential pattern mining process, redundant sequential patterns are identified if the pattern is a sub-sequence of other sequential patterns. A list of compact sequential patterns is generated excluding redundant patterns and used as representative features for the second stage. In the second stage, a sequence similarity measurement is used to evaluate partial similarity between sequences and patterns. Finally, a particle swarm optimization-AdaBoost (PSO-AB) sequence classifier is developed to improve sequence classification accuracy. In the PSO-AB sequence classifier, the PSO algorithm is used to optimize the weights in the individual sequence classifier, while the AdaBoost strategy is used to adaptively change the distribution of patterns that are hard to classify. The experiments show that the proposed two-stage SPM-based sequence classification method is efficient and superior to other approaches.     

Mining Surprising Periodic Patterns

In this paper, we focus on mining surprising periodic patterns in a sequence of events. In many applications, e.g., computational biology, an infrequent pattern is still considered very significant if its actual occurrence frequency exceeds the prior expectation by a large margin. The traditional metric, such as support, is not necessarily the ideal model to measure this kind of surprising patterns because it treats all patterns equally in the sense that every occurrence carries the same weight towards the assessment of the significance of a pattern regardless of the probability of occurrence. A more suitable measurement, information, is introduced to naturally value the degree of surprise of each occurrence of a pattern as a continuous and monotonically decreasing function of its probability of occurrence. This would allow patterns with vastly different occurrence probabilities to be handled seamlessly. As the accumulated degree of surprise of all repetitions of a pattern, the concept of information gain is proposed to measure the overall degree of surprise of the pattern within a data sequence. The bounded information gain property is identified to tackle the predicament caused by the violation of the downward closure property by the information gain measure and in turn provides an efficient solution to this problem. Furthermore, the user has a choice between specifying a minimum information gain threshold and choosing the number of surprising patterns wanted. Empirical tests demonstrate the efficiency and the usefulness of the proposed model.     

Discovering multi-label temporal patterns in sequence databases

Sequential pattern mining is one of the most important data mining techniques. Previous research on mining sequential patterns discovered patterns from point-based event data, interval-based event data, and hybrid event data. In many real life applications, however, an event may involve many statuses; it might not occur only at one certain point in time or over a period of time. In this work, we propose a generalized representation of temporal events. We treat events as multi-label events with many statuses, and introduce an algorithm called MLTPM to discover multi-label temporal patterns from temporal databases. The experimental results show that the efficiency and scalability of the MLTPM algorithm are satisfactory. We also discuss interesting multi-label temporal patterns discovered when MLTPM was applied to historical Nasdaq data.     

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.

     

基于多尺度的时序数据部分周期模式增量挖掘

针对动态时序数据部分周期模式挖掘过程存在的计算复杂度过高和扩展性差等问题,提出了一种结合多尺度理论的时间序列部分周期模式挖掘算法（MSI-PPPGrowth）,所提算法充分利用了时序数据客观存在的时间多尺度特性,将多尺度理论引入时序数据的部分周期模式挖掘过程。首先,将尺度划分后的原始数据以及增量时序数据作为更细粒度的基准尺度数据集进行独立挖掘;然后,利用不同尺度数据间的相关性实现尺度转换,以间接获取动态更新后的数据集对应的全局频繁模式,从而避免了原始数据集的重复扫描和树结构的不断调整。其中,基于克里金法并考虑时序周期性设计了一个新的频繁缺失计数估计模型（PJK-EstimateCount）,以有效估计在尺度转换过程中的缺失项支持度计数。实验结果表明,MSI-PPPGrowth具有良好的可扩展性和实时性,尤其是对于稠密数据集,其性能优势更为突出。     

基于聚类和偏序序列的API用法模式挖掘

在软件开发过程中,开发人员经常需要遵循特定的API用法模式,而这些用法模式几乎没有相关文档作为参考。为了挖掘API用法模式,提出基于聚类和频繁闭合偏序序列的API用法模式挖掘途径。通过抽象语法树对源代码进行解析,对提取API方法调用序列进行层次聚类,最后使用频繁闭合偏序挖掘算法DFP进行API用法模式的挖掘。实验结果表明,在相同的数据集上,与SPADE算法和BIDE算法相比,所得候选API用法模式集更加精简。     

基于相邻事件概率统计的流程挖掘方法

为了提高流程挖掘的准确性和抗噪性,针对目前流程挖掘的基本结构有限、抗噪能力弱、计算耗时长等问题,提出了一种基于相邻事件概率统计的流程挖掘方法。该方法基于挖掘规则,仅需做一次日志遍历和矩阵的简单运算,就可生成挖掘的流程模型。与α算法和启发式算法的实验验证结果表明,该算法不仅能够挖掘顺序、选择、并行、短循环、递归等流程基本结构,而且具有计算复杂度低、抗噪能力强等优势。     

Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events

Previous sequential pattern mining studies have dealt with either point-based event sequences or interval-based event sequences. In some applications, however, event sequences may contain both point-based and interval-based events. These sequences are called hybrid event sequences. Since the relationships among both kinds of events are more diversiform, the information obtained by discovering patterns from these events is more informative. In this study we introduce a hybrid temporal pattern mining problem and develop an algorithm to discover hybrid temporal patterns from hybrid event sequences. We carry out an experiment using both synthetic and real stock price data to compare our algorithm with the traditional algorithms designed exclusively for mining point-based patterns or interval-based patterns. The experimental results indicate that the efficiency of our algorithm is satisfactory. In addition, the experiment also shows that the predicting power of hybrid temporal patterns is higher than that of point-based or interval-based patterns.     

Mining sequential patterns with periodic wildcard gaps

Mining frequent patterns with periodic wildcard gaps is a critical data mining problem to deal with complex real-world problems. This problem can be described as follows: given a subject sequence, a pre-specified threshold, and a variable gap-length with wildcards between each two consecutive letters. The task is to gain all frequent patterns with periodic wildcard gaps. State-of-the-art mining algorithms which use matrices or other linear data structures to solve the problem not only consume a large amount of memory but also run slowly. In this study, we use an Incomplete Nettree structure (the last layer of a Nettree which is an extension of a tree) of a sub-pattern P to efficiently create Incomplete Nettrees of all its super-patterns with prefix pattern P and compute the numbers of their supports in a one-way scan. We propose two new algorithms, MAPB (Mining sequentiAl Pattern using incomplete Nettree with Breadth first search) and MAPD (Mining sequentiAl Pattern using incomplete Nettree with Depth first search), to solve the problem effectively with low memory requirements. Furthermore, we design a heuristic algorithm MAPBOK (MAPB for tOp-K) based on MAPB to deal with the Top-K frequent patterns for each length. Experimental results on real-world biological data demonstrate the superiority of the proposed algorithms in running time and space consumption and also show that the pattern matching approach can be employed to mine special frequent patterns effectively.     

Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree

Sequential mining is the process of applying data mining techniques to a sequential database for the purposes of discovering the correlation relationships that exist among an ordered list of events. An important application of sequential mining techniques is web usage mining, for mining web log accesses, where the sequences of web page accesses made by different web users over a period of time, through a server, are recorded. Web access pattern tree (WAP-tree) mining is a sequential pattern mining technique for web log access sequences, which first stores the original web access sequence database on a prefix tree, similar to the frequent pattern tree (FP-tree) for storing non-sequential data. WAP-tree algorithm then, mines the frequent sequences from the WAP-tree by recursively re-constructing intermediate trees, starting with suffix sequences and ending with prefix sequences.This paper proposes a more efficient approach for using the WAP-tree to mine frequent sequences, which totally eliminates the need to engage in numerous re-construction of intermediate WAP-trees during mining. The proposed algorithm builds the frequent header node links of the original WAP-tree in a pre-order fashion and uses the position code of each node to identify the ancestor/descendant relationships between nodes of the tree. It then, finds each frequent sequential pattern, through progressive prefix sequence search, starting with its first prefix subsequence event. Experiments show huge performance gain over the WAP-tree technique.     

Mining weighted sequential patterns in a sequence database with a time-interval weight

Sequential pattern mining, including weighted sequential pattern mining, has been attracting much attention since it is one of the essential data mining tasks with broad applications. The weighted sequential pattern mining aims to find more interesting sequential patterns, considering the different significance of each data element in a sequence database. In the conventional weighted sequential pattern mining, usually pre-assigned weights of data elements are used to get the importance, which are derived from their quantitative information and their importance in real world application domains. In general sequential pattern mining, the generation order of data elements is considered to find sequential patterns. However, their generation times and time-intervals are also important in real world application domains. Therefore, time-interval information of data elements can be helpful in finding more interesting sequential patterns. This paper presents a new framework for finding time-interval weighted sequential (TiWS) patterns in a sequence database and time-interval weighted support (TiW-support) to find the TiWS patterns. In addition, a new method of mining TiWS patterns in a sequence database is also presented. In the proposed framework of TiWS pattern mining, the weight of each sequence in a sequence database is first obtained from the time-intervals of elements in the sequence, and subsequently TiWS patterns are found considering the weight. A series of evaluation results shows that TIWS pattern mining is efficient and helpful in finding more interesting sequential patterns.