Frequent Sequential Patterns Research Articles

Mining Sequential Trees in a Tree Sequence Database

Tree structures are used extensively in domains such as XML data management, web log analysis, biological computing, and so on. In this paper we introduce the problem of mining frequent sequential trees in a large tree sequence database. We present a framework for mining frequent sequential trees in a so-called tree sequence database. Basically, this framework employs a transformation-based approach which converts the sequential tree mining problem into a traditional sequence mining problem. Our approach firstly mines frequent trees in the tree sequence database. Secondly, we perform a database transformation by means of tree-containment computation to generate a sequence database. Thirdly, after the transformation, frequent sequence patterns can be mined in the newly created sequence database using a conventional sequence mining technique. Finally we perform an inverted transformation process on the output of sequence mining to obtain sequential tree patterns. Experimental results on synthetic datasets show that the proposed framework is both effective and efficient in finding frequent sequential trees in a large tree sequence database.

Mining Web Access Patterns using Root-set of Suffix Trees

With the rapidly growing uses of World Wide Web for various important and sensitive purposes it becomes a sensible necessity to find out the interesting web access patterns from the web access sequences tracked by users frequently. Web access sequential patterns can be used to achieve business intelligence for e-commerce sites and also can be used to analyze system performance. This paper proposes a more efficient web mining algorithm which mines all the sequential patterns from the web access sequences and totally eliminates the concept of linking between nodes. The algorithm uses the aggregate tree structure for mining and then mines from the tree using RST (Root-set of Suffix Trees) for same prefix items. The algorithm finds the frequent sequential patterns by recursively traversing the tree from root-nodes to child-nodes for the length-1 frequent items. The proposed approach doesn’t need to generate any projected tree; it needs only the root-set for each prefix that got in previous step. Experimental results show huge performance gain over the FOF and WAPtree mining techniques by considerably reducing the mining time.

Splitter

Driven by the advance of positioning technology and the popularity of location-sharing services, semantic-enriched trajectory data have become unprecedentedly available. The sequential patterns hidden in such data, when properly defined and extracted, can greatly benefit tasks like targeted advertising and urban planning. Unfortunately, classic sequential pattern mining algorithms developed for transactional data cannot effectively mine patterns in semantic trajectories, mainly because the places in the continuous space cannot be regarded as independent "items". Instead, similar places need to be grouped to collaboratively form frequent sequential patterns. That said, it remains a challenging task to mine what we call fine-grained sequential patterns , which must satisfy spatial compactness, semantic consistency and temporal continuity simultaneously. We propose Splitter to effectively mine such fine-grained sequential patterns in two steps. In the first step, it retrieves a set of spatially coarse patterns, each attached with a set of trajectory snippets that precisely record the pattern's occurrences in the database. In the second step, Splitter breaks each coarse pattern into fine-grained ones in a top-down manner, by progressively detecting dense and compact clusters in a higher-dimensional space spanned by the snippets. Splitter uses an effective algorithm called weighted snippet shift to detect such clusters, and leverages a divide-and-conquer strategy to speed up the top-down pattern splitting process. Our experiments on both real and synthetic data sets demonstrate the effectiveness and efficiency of Splitter.

Mining Probabilistically Frequent Sequential Patterns in Large Uncertain Databases

Data uncertainty is inherent in many real-world applications such as environmental surveillance and mobile tracking. Mining sequential patterns from inaccurate data, such as those data arising from sensor readings and GPS trajectories, is important for discovering hidden knowledge in such applications. In this paper, we propose to measure pattern frequentness based on the possible world semantics. We establish two uncertain sequence data models abstracted from many real-life applications involving uncertain sequence data, and formulate the problem of mining probabilistically frequent sequential patterns (or p-FSPs) from data that conform to our models. However, the number of possible worlds is extremely large, which makes the mining prohibitively expensive. Inspired by the famous PrefixSpan algorithm, we develop two new algorithms, collectively called U-PrefixSpan, for p-FSP mining. U-PrefixSpan effectively avoids the problem of “possible worlds explosion”, and when combined with our four pruning and validating methods, achieves even better performance. We also propose a fast validating method to further speed up our U-PrefixSpan algorithm. The efficiency and effectiveness of U-PrefixSpan are verified through extensive experiments on both real and synthetic datasets.

Improving Efficiency of Apriori Algorithms for Sequential Pattern Mining

Computer Systems are exposed to an increasing number of different types of security threats due to the expanding of internet in recent years. How to detect network intrusions effectively becomes an important security technique. Many intrusions aren't composed by single events, but by a series of attack steps taken in chronological order. Analyzing the order in which events occur can improve the attack detection accuracy and reduce false alarms. Intrusion is a multi step process in which a number of events must occur sequentially in order to launch a successful attack. Intrusion detection using sequential pattern mining is a research topic focusing on the field of information security. Sequential Pattern Mining is used to discover the frequent sequential pattern in the event dataset. Sequential Pattern mining algorithms can be broadly classified into Apriori based, Pattern growth based and a combination of both. The first algorithm is based on the characteristic of Apriori and the second uses a pattern growth approach. The major drawback of the Apriori based algorithm is the multiple scans of the database, generating maximal patterns. In this paper, a simulation study of both the algorithms, a modified AprioriALL Algorithm to optimize the processing by including set theory techniques and the original AprioriALL algorithm is done on a network intrusion dataset from KDD cup 1999. Experimental results show that the modified algorithm shrinks the dataset size. At the most, it also scans the database twice. Also, as the interestingness of the itemset is increased with the dataset shrinking it leads to efficient sequences with high associativity. As the database is reduced, the time taken to mine sequences also reduces and is faster than Apriori based algorithm.

Detecting App-DDoS Attacks Based on Maximal Frequent Sequential Pattern Mining

Detecting App-DDoS Attacks Based on Maximal Frequent Sequential Pattern Mining

Efficient Analysis of Pattern and Association Rule Mining Approaches

The process of data mining produces various patterns from a given data source. The most recognized data mining tasks are the process of discovering frequent itemsets, frequent sequential patterns, frequent sequential rules and frequent association rules. Numerous efficient algorithms have been proposed to do the above processes. Frequent pattern mining has been a focused topic in data mining research with a good number of references in literature and for that reason an important progress has been made, varying from performant algorithms for frequent itemset mining in transaction databases to complex algorithms, such as sequential pattern mining, structured pattern mining, correlation mining. Association Rule mining (ARM) is one of the utmost current data mining techniques designed to group objects together from large databases aiming to extract the interesting correlation and relation among huge amount of data. In this article, we provide a brief review and analysis of the current status of frequent pattern mining and discuss some promising research directions. Additionally, this paper includes a comparative study between the performance of the described approaches.

A Survey Paper on Trajectory Pattern Mining for Pattern Matching Query

amount information of moving objects on road network is being collected with the help of various recent technologies. The tracking of these moving objects on road networks is becoming important because of it's application in various areas. Classification has been used for classifying various kinds of data sets like graph, text documents. However, there is a lack of study on data like trajectories on road networks. Data mining techniques, especially, sequential pattern mining can be used to extract frequent spatio-temporal patterns. Again it needs to confine the length of sequential patterns to ensure high efficiency. After extracting frequent sequential patterns in trajectories, classification can be applied to classify patterns which provide useful information in applications such as city and transportation planning, road construction, design, and maintenance, marketing sector. In this paper, whole pattern matching query concept is adopted after the classification to find total traffic volume on given trajectory edge. At the same time, user can find number of vehicles moving in one as well as in both directions on that particular trajectory. Keywordssequential patterns, frequent pattern based classification, location based services, pattern matching.

Multiple Buffering for Parallel Approximate Sequence Matching using Disk-based Suffix Tree on Multi-core CPU

AbstractSuffix trees, which are trie structures that present the suffixes of sequences (e.g., strings), are widely used for sequence search in different application domains such as, text data mining, bioinformatics and computational biology. In particular, suffix trees are useful in bioinformatics applications, because they can search similar sub-sequences and extract frequent sequence patterns efficiently. In recent years, efficient construction of a suffix tree that allows faster sequence searches has become one of the most important challenges, because the number and size of the data that are stored in sequence databases have been increasing exponentially. This paper proposes a novel parallelization model for approximate sequence matching that uses disk-based suffix trees, which are built on hard disks not on memory, on a multi-core CPU. In the proposed parallelization model, we divide an entire sequence database into two or more sub-databases called partitions. For each partition, we build a disk-based suffix tree and define a task as an approximate sequence matching on one disk-based suffix tree. Moreover, the proposed parallelization model involves a multiple buffering management system to avoid conflicts among CPU-cores. We evaluated the proposed parallelization model using an actual amino acid sequence database on a PC. The experimental results show a substantial improvement in computation performance.

Mining the Change of Customer Behavior in Fuzzy Time-Interval Sequential Patterns with Aid of Similarity Computation Index (SCI) and Genetic Algorithm (GA)

Sequential Pattern Mining is closely related to the concept of Data Mining. It is the process of discovering the frequent sequential patterns in a given database. Even though, various algorithms and techniques are used in the process of sequential pattern mining. One of the Mine Fuzz Change model was perform the Sequential Pattern Mining process by using SCI (Similarity Computation Index) and successfully perform the pattern classification process. But this model has the drawback in the SCI computation. Because, the SCI value is computed by using the raw data which is collected from different time interval and it creates the time complexity in the pattern mining process. To solve this drawback, in this paper, an optimized fuzzy time interval sequential pattern mining algorithm is proposed. The proposed method finds the customer behavior changes in the fuzzy time interval sequential patterns by exploiting the optimized FTI algorithm and the patterns are classified based on their support and SCI (Similarity Computation Index) value. Our proposed method performance is evaluated by conducting different experiments on the synthetic dataset. Moreover, our proposed method performance is compared with the existing Mine Fuzz Change model. The performance results shows that our proposed method reduces the time complexity and it will helps managers to understand the changing behaviors of their customers

PTree: Mining Sequential Patterns Efficiently in Multiple Data Streams Environment

Although issues of data streams have been widely studied and utilized, it is nevertheless challenging to deal with sequential mining of data streams. In this paper, we assume that the transaction of a user is partially coming and that there is no auxiliary for buffering and integrating. We adopt the Path Tree for mining frequent sequential patterns over data streams and integrate the user's sequences efficiently. Algorithms with regards to accuracy (PAlgorithm) and space (PSAlgorithm) are proposed to meet the different aspects of users, although GAlgorithm for mining frequent sequential patterns with a gap limitation is proposed. Many pruning properties are used to further reduce the space usage and improve the accuracy of our algorithms. We also prove that PAlgorithm mine frequent sequential patterns with the approximate support of error guarantee. Through thoughtful experiments, synthetic and real datasets are utilized to verify the feasibility of our algorithms.

SeqNLS: Nuclear Localization Signal Prediction Based on Frequent Pattern Mining and Linear Motif Scoring

Nuclear localization signals (NLSs) are stretches of residues in proteins mediating their importing into the nucleus. NLSs are known to have diverse patterns, of which only a limited number are covered by currently known NLS motifs. Here we propose a sequential pattern mining algorithm SeqNLS to effectively identify potential NLS patterns without being constrained by the limitation of current knowledge of NLSs. The extracted frequent sequential patterns are used to predict NLS candidates which are then filtered by a linear motif-scoring scheme based on predicted sequence disorder and by the relatively local conservation (IRLC) based masking.The experiment results on the newly curated Yeast and Hybrid datasets show that SeqNLS is effective in detecting potential NLSs. The performance comparison between SeqNLS with and without the linear motif scoring shows that linear motif features are highly complementary to sequence features in discerning NLSs. For the two independent datasets, our SeqNLS not only can consistently find over 50% of NLSs with prediction precision of at least 0.7, but also outperforms other state-of-the-art NLS prediction methods in terms of F1 score or prediction precision with similar or higher recall rates. The web server of the SeqNLS algorithm is available at http://mleg.cse.sc.edu/seqNLS.

A Neoteric Web Recommender System based on Approach of Mining Frequent Sequential Pattern from Customized Web Log Preprocessing

A Neoteric Web Recommender System based on Approach of Mining Frequent Sequential Pattern from Customized Web Log Preprocessing

Mining Compressing Sequential Patterns

AbstractPattern mining based on data compression has been successfully applied in many data mining tasks. For itemset data, the Krimp algorithm based on the minimum description length (MDL) principle was shown to be very effective in solving the redundancy issue in descriptive pattern mining. However, for sequence data, the redundancy issue of the set of frequent sequential patterns is not fully addressed in the literature. In this article, we study MDL‐based algorithms for mining non‐redundant sets of sequential patterns from a sequence database. First, we propose an encoding scheme for compressing sequence data with sequential patterns. Second, we formulate the problem of mining the most compressing sequential patterns from a sequence database. We show that this problem is intractable and belongs to the class of inapproximable problems. Therefore, we propose two heuristic algorithms. The first of these uses a two‐phase approach similar to Krimp for itemset data. To overcome performance issues in candidate generation, we also propose GoKrimp, an algorithm that directly mines compressing patterns by greedily extending a pattern until no additional compression benefit of adding the extension into the dictionary. Since checks for additional compression benefit of an extension are computationally expensive we propose a dependency test which only chooses related events for extending a given pattern. This technique improves the efficiency of the GoKrimp algorithm significantly while it still preserves the quality of the set of patterns. We conduct an empirical study on eight datasets to show the effectiveness of our approach in comparison to the state‐of‐the‐art algorithms in terms of interpretability of the extracted patterns, run time, compression ratio, and classification accuracy using the discovered patterns as features for different classifiers. © 2013 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2013

A data mining approach to discovering reliable sequential patterns

Sequential pattern mining is a data mining method for obtaining frequent sequential patterns in a sequential database. Conventional sequence data mining methods could be divided into two categories: Apriori-like methods and pattern growth methods. In a sequential pattern, probability of time between two adjacent events could provide valuable information for decision-makers. As far as we know, there has been no methodology developed to extract this probability in the sequential pattern mining process. We extend the PrefixSpan algorithm and propose a new sequential pattern mining approach: P-PrefixSpan. Besides minimum support-count constraint, this approach imposes minimum time-probability constraint, so that fewer but more reliable patterns will be obtained. P-PrefixSpan is compared with PrefixSpan in terms of number of patterns obtained and execution efficiency. Our experimental results show that P-PrefixSpan is an efficient and scalable method for sequential pattern mining.

스트림 데이터에서 동적 가중치를 이용한 순차 패턴 탐사 기법

순차 패턴 탐사 기법은 순서를 갖는 패턴들의 집합 중에 빈발하게 발생하는 패턴을 탐사하는 기법이다. 순차 패턴 탐사 분야 중에 동적 가중치 순차 패턴 탐사는 가중치가 시간에 따라 변화하는 컴퓨팅 환경에 적용 가능한 탐사 기법으로 동적인 가중치 변화를 탐색 과정에 적용하여 다양한 환경에서 활용 가능하다. 이 논문에서는 다양한 순차 데이터가 들어오는 스트림 환경에서 동적 가중치를 적용하여 빈발한 이벤트들을 탐사하는 새로운 순차 패턴 탐사 기법을 제안한다. 제안하는 기법은 시간 순서에 의한 상대적인 동적 가중치를 사용하여 탐색해야 하는 후보 패턴을 줄여주고 해시 구조를 통한 데이터 입출력으로 빈발한 순차 패턴을 빠르게 탐사할 수 있다. 이 기법을 사용하면 기존 가중치를 적용하는 방식보다 메모리 사용과 처리 시간을 줄여줘 매우 효율적이다. 제안하는 기법은 다른 가중치 순차 패턴 탐사 기법과의 비교를 통해 동적 가중치 탐사 기법의 중요성을 보인다. A sequential pattern mining is finding out frequent patterns from the data set in time order. In this field, a dynamic weighted sequential pattern mining is applied to a computing environment that changes depending on the time and it can be utilized in a variety of environments applying changes of dynamic weight. In this paper, we propose a new sequence data mining method to explore the stream data by applying the dynamic weight. This method reduces the candidate patterns that must be navigated by using the dynamic weight according to the relative time sequence, and it can find out frequent sequence patterns quickly as the data input and output using a hash structure. Using this method reduces the memory usage and processing time more than applying the existing methods. We show the importance of dynamic weighted mining through the comparison of different weighting sequential pattern mining techniques.

Sequential Pattern Tree Mining

Sequential pattern mining, which discovers the correlation relationships from the ordered list of events, is an important research field in data mining area. In our study, we have developed a Sequential Pattern Tree structure to store both frequent and non-frequent items from sequence database. It requires only one scan of database to build the tree due to storage of non-frequent items which reduce the tree construction time considerably. Then, we have proposed an efficient Sequential Pattern Tree Mining algorithm which can generate frequent sequential patterns from the Sequential Pattern Tree recursively. The main advantage of this algorithm is to mine the complete set of frequent sequential patterns from the Sequential Pattern Tree without generating any intermediate projected tree. Again, it does not generate unnecessary candidate sequences and not require repeated scanning of the original database. We have compared our proposed approach with three existing algorithms and our performance study shows that, our algorithm is much faster than apriori based GSP algorithm and also faster than existing PrefixSpan and Tree Based Mining algorithm which are based on pattern growth approaches.

K-means Sequence Clustering Algorithm based on Top-k Maximal Frequent Sequence Patterns

K-means Sequence Clustering Algorithm based on Top-k Maximal Frequent Sequence Patterns

Analyzing consumers’ shopping behavior using RFID data and pattern mining

The development of sensor networks has enabled detailed tracking of customer behavior in stores. Shopping path data which records each customer’s position and time information is attracting attention as new marketing data. However, there are no proposed marketing models which can identify good customers from huge amounts of time series data on customer movement in the store. This research aims to use shopping path data resulting from tracking customer behavior in the store, using information on the sequence of visiting each product zone in the store and staying time at each product zone, to find how they affect purchasing. To discover useful knowledge for store management, shopping paths data has been transformed into sequence data including information on visit sequence and staying times in the store, and LCMseq has been applied to them to extract frequent sequence patterns. In this paper, we find characteristic in-store behavior patterns of good customers by using actual data of a Japanese supermarket.

Graph based Approach for Mining Frequent Sequential Access Patterns of Web pages

The Internet has impacted almost every aspect of our society. Since the number of web sites and web pages has grown rapidly, discovering and understanding web users’ surfing behavior are essential for the development of successful web monitoring and recommendation systems. To capture users’ web access behavior, one promising approach is web usage mining which discovers interesting and frequent user access patterns from web logs. Sequential Web page Access pattern mining has been a focused theme in data mining research for over a decade with wide range of applications. The aim of discovering frequent sequential access (usage) patterns in Web log data is to obtain information about the navigational behavior of the users. This can be used for advertising purposes, for creating dynamic user profiles etc. We propose a new approach for mining the web usage data by creating graph using web access sequence of sorted web log and mining the useful sequential access pattern.