NetNMSP: Nonoverlapping maximal sequential pattern mining

Applied Intelligence - Nonoverlapping sequential pattern mining, as a kind of repetitive sequential pattern mining with gap constraints, can find more valuable patterns. Traditional algorithms...     

On mining multi-time-interval sequential patterns

Sequential pattern mining is essential in many applications, including computational biology, consumer behavior analysis, web log analysis, etc. Although sequential patterns can tell us what items are frequently to be purchased together and in what order, they cannot provide information about the time span between items for decision support. Previous studies dealing with this problem either set time constraints to restrict the patterns discovered or define time-intervals between two successive items to provide time information. Accordingly, the first approach falls short in providing clear time-interval information while the second cannot discover time-interval information between two non-successive items in a sequential pattern. To provide more time-related knowledge, we define a new variant of time-interval sequential patterns, called multi-time-interval sequential patterns, which can reveal the time-intervals between all pairs of items in a pattern. Accordingly, we develop two efficient algorithms, called the MI-Apriori and MI-PrefixSpan algorithms, to solve this problem. The experimental results show that the MI-PrefixSpan algorithm is faster than the MI-Apriori algorithm, but the MI-Apriori algorithm has better scalability in long sequence data.     

多维序列模式挖掘算法

提出一种基于最大频繁模式、模式相似与属性描述相结合的多维序列模式挖掘算法MSP,该算法包括3个步骤:挖掘数据集中的最大频繁模式,每个频繁模式成为一个模式类;比较数据中各序列项序列与各模式类的包含与相似关系;按照一定的规则抽取与各模式类相关的属性,给出以属性为前件、模式类为后件的多维序列规则为形式的多维序列模式挖掘结果....     

An incremental mining algorithm for maintaining sequential patterns using pre-large sequences

Mining useful information and helpful knowledge from large databases has evolved into an important research area in recent years. Among the classes of knowledge derived, finding sequential patterns in temporal transaction databases is very important since it can help model customer behavior. In the past, researchers usually assumed databases were static to simplify data-mining problems. In real-world applications, new transactions may be added into databases frequently. Designing an efficient and effective mining algorithm that can maintain sequential patterns as a database grows is thus important. In this paper, we propose a novel incremental mining algorithm for maintaining sequential patterns based on the concept of pre-large sequences to reduce the need for rescanning original databases. Pre-large sequences are defined by a lower support threshold and an upper support threshold that act as gaps to avoid the movements of sequences directly from large to small and vice versa. The proposed algorithm does not require rescanning original databases until the accumulative amount of newly added customer sequences exceeds a safety bound, which depends on database size. Thus, as databases grow larger, the numbers of new transactions allowed before database rescanning is required also grow. The proposed approach thus becomes increasingly efficient as databases grow.     

一种约束最大频繁模式快速挖掘算法

为了避免用户通过"二次挖掘"才能得到有用的结果集,本文提出了一种新的约束最大频繁模式挖掘算法CSMFPMax.CSMFP-Max算法基于CFP树和对称矩阵,在挖掘过程中采用了多种剪枝策略并结合了自顶向下和自底向上的双向搜索策略,大大缩小了候选集规模,避免了不必要的条件CFP树的产生.理论分析和实验结果表明CSMFP-Max算法是一种高效的约束最大频繁模式挖掘算法,具有良好的时空效率.     

A novel approach for mining maximal frequent patterns

Mining maximal frequent patterns (MFPs) is an approach that limits the number of frequent patterns (FPs) to help intelligent systems operate efficiently. Many approaches have been proposed for mining MFPs, but the complexity of the problem is enormous. Therefore, the run time and memory usage are still large. Recently, the N-list structure has been proposed and verified to be very effective for mining FPs, frequent closed patterns, and top-rank-k FPs. Therefore, this paper uses the N-list structure for mining MFPs. A pruning technique is also proposed to prune branches to reduce the search space. This technique is applied to an algorithm called INLA-MFP (improved N-list-based algorithm for mining maximal frequent patterns) for mining MFPs. Experiments were conducted to evaluate the effectiveness of the proposed algorithm. The experimental results show that INLA-MFP outperforms two state-of-the-art algorithms for mining MFPs.     

序列模式增量式更新的研究

序列模式挖掘是数据挖掘领域中十分重要的研究课题.目前已有许多算法用于序列模式的挖掘,但在序列模式增量式更新方面的研究还比较少,针对这种情况提出了序列模式增量式更新的挖掘算法SPIU.SPIU算法充分利用了原有的挖掘结果,并对产生的候选频繁序列进行剪枝,有效地减小了候选频繁序列的大小,从而很好地改善了挖掘效率.测试结果表明SPIU算法是正确和高效的,另外算法还具有很好的扩放性.     

Hybridized optimization oriented fast negative sequential patterns mining

Multimedia Tools and Applications - Recently, negative sequential patterns (NSP) (like missing medical treatments) mining is important in data mining research since it includes negative...     

Mining sequential patterns across multiple sequence databases

In this paper, given a set of sequence databases across multiple domains, we aim at mining multi-domain sequential patterns, where a multi-domain sequential pattern is a sequence of events whose occurrence time is within a pre-defined time window. We first propose algorithm Naive in which multiple sequence databases are joined as one sequence database for utilizing traditional sequential pattern mining algorithms (e.g., PrefixSpan). Due to the nature of join operations, algorithm Naive is costly and is developed for comparison purposes. Thus, we propose two algorithms without any join operations for mining multi-domain sequential patterns. Explicitly, algorithm IndividualMine derives sequential patterns in each domain and then iteratively combines sequential patterns among sequence databases of multiple domains to derive candidate multi-domain sequential patterns. However, not all sequential patterns mined in the sequence database of each domain are able to form multi-domain sequential patterns. To avoid the mining cost incurred in algorithm IndividualMine, algorithm PropagatedMine is developed. Algorithm PropagatedMine first performs one sequential pattern mining from one sequence database. In light of sequential patterns mined, algorithm PropagatedMine propagates sequential patterns mined to other sequence databases. Furthermore, sequential patterns mined are represented as a lattice structure for further reducing the number of sequential patterns to be propagated. In addition, we develop some mechanisms to allow some empty sets in multi-domain sequential patterns. Performance of the proposed algorithms is comparatively analyzed and sensitivity analysis is conducted. Experimental results show that by exploring propagation and lattice structures, algorithm PropagatedMine outperforms algorithm IndividualMine in terms of efficiency (i.e., the execution time).     

An efficient algorithm for mining periodic high-utility sequential patterns

A periodic high-utility sequential pattern (PHUSP) is a pattern that not only yields a high-utility (e.g. high profit) but also appears regularly in a sequence database. Finding PHUSPs is useful for several applications such as market basket analysis, where it can reveal recurring and profitable customer behavior. Although discovering PHUSPs is desirable, it is computationally difficult. To discover PHUSPs efficiently, this paper proposes a structure for periodic high-utility sequential pattern mining (PHUSPM) named PUSP. Furthermore, to reduce the search space and speed up PHUSPM, a pruning strategy is developed. This results in an efficient algorithm called periodic high-utility sequential pattern optimal miner (PUSOM). An experimental evaluation was performed on both synthetic and real-life datasets to compare the performance of PUSOM with state-of-the-art PHUSPM algorithms in terms of execution time, memory usage and scalability. Experimental results show that the PUSOM algorithm can efficiently discover the complete set of PHUSPs. Moreover, it outperforms the other four algorithms as the former can prune many unpromising patterns using its designed structure and pruning strategy.     

Fast incremental mining of web sequential patterns with PLWAP tree

Point and click at web pages generate continuous data sequences, which flow into the web log data, causing the need to update previously mined web sequential patterns. Algorithms for mining web sequential patterns from scratch include WAP, PLWAP and Apriori-based GSP. Reusing old patterns with only recent additional data sequences in an incremental fashion, when updating patterns, would achieve fast response time with reasonable memory space usage. This paper proposes two algorithms, RePL4UP (Revised PLWAP For UPdate), and PL4UP (PLWAP For UPdate), which use the PLWAP tree structure to incrementally update web sequential patterns efficiently without scanning the whole database even when previous small items become frequent. The RePL4UP concisely stores the position codes of small items in the database sequences in its metadata during tree construction. During mining, RePL4UP scans only the new additional database sequences, revises the old PLWAP tree to restore information on previous small items that have become frequent, while it deletes previous frequent items that have become small using the small item position codes. PL4UP initially builds a bigger PLWAP tree that includes all sequences in the database using a tolerance support, t, that is lower than the regular minimum support, s. The position code features of the PLWAP tree are used to efficiently mine these trees to extract current frequent patterns when the database is updated. These approaches more quickly update old frequent patterns without the need to re-scan the entire updated database.     

SPEck: mining statistically-significant sequential patterns efficiently with exact sampling

Data Mining and Knowledge Discovery - We study the problem of efficiently mining statistically-significant sequential patterns from large datasets, under different null models. We consider one null...     

一种挖掘带时间约束序列模式的改进算法

针对带时间约束的序列模式，提出了一种改进的挖掘算法TSPM，克服了传统的序列模式挖掘方法时空开销大，结果数量巨大且缺少针对性的缺陷．算法引入图结构表示频繁2序列，仅需扫描一次数据库，即可将与挖掘任务相关的信息映射到图中，图结构的表示使得挖掘过程可以充分利用项目之间的次序关系，提高了频繁序列的生成效率．另外算法利用序列的位置信息计算支持度，降低了处理时间约束的复杂性，避免了反复测试序列包含的过程．实验证明，该算法较传统的序列模式发现算法在时间和空间性能上具有优越性。     

Privacy preserving data mining of sequential patterns for network traffic data

As the total amount of traffic data in networks has been growing at an alarming rate, there is currently a substantial body of research that attempts to mine traffic data with the purpose of obtaining useful information. For instance, there are some investigations into the detection of Internet worms and intrusions by discovering abnormal traffic patterns. However, since network traffic data contain information about the Internet usage patterns of users, network users’ privacy may be compromised during the mining process. In this paper, we propose an efficient and practical method that preserves privacy during sequential pattern mining on network traffic data. In order to discover frequent sequential patterns without violating privacy, our method uses the N-repository server model, which operates as a single mining server and the retention replacement technique, which changes the answer to a query probabilistically. In addition, our method accelerates the overall mining process by maintaining the meta tables in each site so as to determine quickly whether candidate patterns have ever occurred in the site or not. Extensive experiments with real-world network traffic data revealed the correctness and the efficiency of the proposed method.     

Scalable and parallel sequential pattern mining using spark

World Wide Web - The performance of the existing parallel sequential pattern mining algorithms is often unsatisfactory due to high IO overhead and imbalanced load among the computing nodes. To...     

Parallel partitioning for distributed systems using sequential assignment

This paper introduces a method to combine the advantages of both task parallelism and fine-grained co-design specialisation to achieve faster execution times than either method alone on distributed heterogeneous architectures. The method uses a novel mixed integer linear programming formalisation to assign code sections from parallel tasks to share computational components with the optimal trade-off between acceleration from component specialism and serialisation delay. The paper provides results for software benchmarks partitioned using the method and formal implementations of previous alternatives to demonstrate both the practical tractability of the linear programming approach and the increase in program acceleration potential deliverable.     

一种有效的基于图遍历的加权序列模式挖掘算法

为解决加权遍历模式挖掘问题,概括了加权有向图的种类,提出一种边加权有向图与顶点加权有向图间的变换模型,并基于该模型提出一种基于图遍历的加权序列模式挖掘算法GTWSPMiner.该算法根据遍历模式中的项的连续性特点,采用一种加权前缀投影序列模式增长方法,将原挖掘序列数据库的任务分解成一组挖掘局部投影数据库的小任务.对比实验结果表明,该算法能快速有效地挖掘加权频繁遍历模式.     

Effective database transformation and efficient support computation for mining sequential patterns

In this paper, we propose a novel algorithm for mining frequent sequences from transaction databases. The transactions of the same customers form a set of customer sequences. A sequence (an ordered list of itemsets) is frequent if the number of customer sequences containing it satisfies the user-specified threshold. The 1-sequence is a special type of sequences because it consists of only a single itemset instead of an ordered list, while the k-sequence is a sequence composed of k itemsets. Compared with the cost of mining frequent k-sequences (k ≥ 2), the cost of mining frequent 1-sequences is negligible. We adopt a two-phase architecture to find the two types of frequent sequences separately in order that the discovery of frequent k-sequences can be well designed and optimized. For efficient frequent k-sequence mining, every frequent 1-sequence is encoded as a unique symbol and the database is transformed into one constituted by the symbols. We find that it is unnecessary to encode all the frequent 1-seqences, and make full use of the discovered frequent 1-sequences to transform the database into one with a smaller size. For every k ≥ 2, the customer sequences in the transformed database are scanned to find all the frequent k-sequences. We devise the compact representation for a customer sequence and elaborate the method to enumerate all distinct subsequences from a customer sequence without redundant scans. The soundness of the proposed approach is verified and a number of experiments are performed. The results show that our approach outperforms the previous works in both scalability and execution time.     

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.