Efficient Sequential Pattern Mining Research Articles

DRIVE: Dockerfile Rule Mining and Violation Detection

A Dockerfile defines a set of instructions to build Docker images, which can then be instantiated to support containerized applications. Recent studies have revealed a considerable amount of quality issues with Dockerfiles. In this article, we propose a novel approach, Dockerfiles Rule mIning and Violation dEtection ( DRIVE ), to mine implicit rules and detect potential violations of such rules in Dockerfiles. DRIVE first parses Dockerfiles and transforms them to an intermediate representation. It then leverages an efficient sequential pattern mining algorithm to extract potential patterns. With heuristic-based reduction and moderate human intervention, potential rules are identified, which can then be utilized to detect potential violations of Dockerfiles. DRIVE identifies 34 semantic rules and 19 syntactic rules including 9 new semantic rules that have not been reported elsewhere. Extensive experiments on real-world Dockerfiles demonstrate the efficacy of our approach.

VEPRECO: Vertical databases with pre-pruning strategies and common candidate selection policies to fasten sequential pattern mining

Sequential pattern mining (SPM) discovers, from event transactions recorded along time, patterns of events fulfilling a sequential order. In this work, we introduce a new efficient sequential pattern mining algorithm called VEPRECO. VEPRECO proposes three main contributions that fasten the mining process: a vertical representation of patterns, pre-pruning strategies to avoid checking infrequent patterns, and common candidate selection policies that reduce the number of iterations performed by the algorithm.An experimental evaluation was performed with synthetic and real-world datasets, and the results have been compared with the most time and memory-efficient sequential pattern mining algorithm in the literature, the CM-SPAM algorithm, which we have taken as a baseline. We analysed separately how each of the proposed contributions affects time and memory usage and found that the one that reduced the most time and memory was the representation of the proposed patterns. Pre-pruning strategies and common candidate selection policies reduce runtime in datasets with many sequences and similar lengths of transactions and sequences.

Efficient sequential pattern mining with wildcards for keyphrase extraction

A keyphrase (a multi-word unit) in a document denotes one or multiple keywords capturing a main topic of the underlying document. Finding good keyphrases of a document can quickly summarize knowledge for efficient decision making and benefit domains involving intensive text information. To date, existing keyphrase extraction methods cannot be customized to each specific document, mainly because their patterns used to form paraphrases are too restrictive and may not capture flexible keyword relationships inside the text. In this paper, we propose a sequential pattern mining based document-specific keyphrase extraction method. Our key innovation is to use wildcards (or gap constraints) to help extract sequential patterns, so the flexible wildcard constraints within a pattern can capture semantic relationships between words, and the system will have full flexibility to discover different types of sequential patterns as candidates for keyphrase extraction. To achieve the goal, we regard each single document as a sequential dataset, and propose an efficient algorithm to mine sequential patterns with wildcard and one-off conditions that allows important keyphrases to be captured during the mining process. For each extracted keyphrase candidate, we use some statistical pattern features to characterize it, and further collect all keyphrases from the document to form a training set. A supervised learning classifier is trained to identify keyphrases from a test document. Because our pattern mining and pattern characterization processes are customized to each single document, keyphases extracted from our method are highly specific for each document. Experimental results demonstrate that the proposed sequential pattern mining method outperforms existing pattern mining methods in both runtime performance and completeness. Comparisons on keyphrase benchmark datasets also confirm that the proposed document-specific keyphrase extraction method is effective in improving the quality of extracted keyphrases.

Efficient Mining of Sequential Patterns in a Sequence Database with Weight Constraint

Sequence pattern mining is one of the essential data mining tasks with broad applications. Many sequence mining algorithms have been developed to find a set of frequent sub-sequences satisfying the support threshold in a sequence database. The main problem in most of these algorithms is they generate huge number of sequential patterns when the support threshold is low and all the sequence patterns are treated uniformly while real sequential patterns have different importance. In this paper, we propose an algorithm which aims to find more interesting sequential patterns, considering the different significance of each data element in a sequence database. Unlike the conventional weighted sequential pattern mining, where the weights of items are preassigned according to the priority or importance, in our approach the weights are set according to the real data and during the mining process not only the supports but also weights of patterns are considered. The experimental results show that the algorithm is efficient and effective in generating more interesting patterns.

Mining sequential patterns for classification

While a number of efficient sequential pattern mining algorithms were developed over the years, they can still take a long time and produce a huge number of patterns, many of which are redundant. These properties are especially frustrating when the goal of pattern mining is to find patterns for use as features in classification problems. In this paper, we describe BIDE-Discriminative, a modification of BIDE that uses class information for direct mining of predictive sequential patterns. We then perform an extensive evaluation on nine real-life datasets of the different ways in which the basic BIDE-Discriminative can be used in real multi-class classification problems, including 1-versus-rest and model-based search tree approaches. The results of our experiments show that 1-versus-rest provides an efficient solution with good classification performance.

An Efficient Algorithm for Sequence Generation in Data Mining

Data mining is the method or the activity of analyzing data from different perspectives and summarizing it into useful information. There are several major data mining techniques that have been developed and are used in the data mining projects which include association , classification , clustering , sequential patterns, prediction and decision tree . Among different tasks in data mining, sequential pattern mining is one of the most important tasks. Sequential pattern mining involves the mining of the subsequences that appear frequently in a set of sequences. It has a variety of applications in several domains such as the analysis of customer purchase patterns, protein sequence analysis, DNA analysis, gene sequence analysis, web access patterns, seismologic data and weather observations. Various models and algorithms have been developed for the efficient mining of sequential patterns in large amount of data. This research paper analyzes the efficiency of three sequence generation algorithms namely GSP, SPADE and PrefixSpan on a retail dataset by applying various performance factors. From the experimental results, it is observed that the PrefixSpan algorithm is more efficient than other two algorithms.

An efficient tree-based algorithm for mining sequential patterns with multiple minimum supports

Sequential pattern mining (SPM) is an important technique for determining time-related behavior in sequence databases. In real-life applications, the frequencies for various items in a sequence database are not exactly equal. If all items are set with the same minimum support, the rare item problem may result, meaning that we are unable to effectively retrieve interesting patterns regardless of whether minsup is set too high or too low. Liu (2006) first included the concept of multiple minimum supports (MMSs) to SPM. It allows users to specify the minimum item support (MIS) for each item according to its natural frequency. A generalized sequential pattern-based algorithm, named Multiple Supports – Generalized Sequential Pattern (MS-GSP), was also developed to mine complete set of sequential patterns. However, the MS-GSP adopts candidate generate-and-test approach, which has been recognized as a costly and time-consuming method in pattern discovery. For the efficient mining of sequential patterns with MMSs, this study first proposes a compact data structure, called a Preorder Linked Multiple Supports tree (PLMS-tree), to store and compress the entire sequence database. Based on a PLMS-tree, we develop an efficient algorithm, Multiple Supports – Conditional Pattern growth (MSCP-growth), to discover the complete set of patterns. The experimental result shows that the proposed approach achieves more preferable findings than the MS-GSP and the conventional SPM.

Efficient Mining of Gap-Constrained Subsequences and Its Various Applications

Mining frequent subsequence patterns is a typical data-mining problem and various efficient sequential pattern mining algorithms have been proposed. In many application domains (e.g., biology), the frequent subsequences confined by the predefined gap requirements are more meaningful than the general sequential patterns. In this article, we propose two algorithms, Gap-BIDE for mining closed gap-constrained subsequences from a set of input sequences, and Gap-Connect for mining repetitive gap-constrained subsequences from a single input sequence. Inspired by some state-of-the-art closed or constrained sequential pattern mining algorithms, the Gap-BIDE algorithm adopts an efficient approach to finding the complete set of closed sequential patterns with gap constraints, while the Gap-Connect algorithm efficiently mines an approximate set of long patterns by connecting short patterns. We also present several methods for feature selection from the set of gap-constrained patterns for the purpose of classification and clustering. Our extensive performance study shows that our approaches are very efficient in mining frequent subsequences with gap constraints, and the gap-constrained pattern based classification/clustering approaches can achieve high-quality results.

An UpDown Directed Acyclic Graph Approach for Sequential Pattern Mining

Traditional pattern growth-based approaches for sequential pattern mining derive length-(k+1) patterns based on the projected databases of length-k patterns recursively. At each level of recursion, they unidirectionally grow the length of detected patterns by one along the suffix of detected patterns, which needs k levels of recursion to find a length-k pattern. In this paper, a novel data structure, UpDown Directed Acyclic Graph (UDDAG), is invented for efficient sequential pattern mining. UDDAG allows bidirectional pattern growth along both ends of detected patterns. Thus, a length-k pattern can be detected in [log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> k + 1] levels of recursion at best, which results in fewer levels of recursion and faster pattern growth. When minSup is large such that the average pattern length is close to 1, UDDAG and PrefixSpan have similar performance because the problem degrades into frequent item counting problem. However, UDDAG scales up much better. It often outperforms PrefixSpan by almost one order of magnitude in scalability tests. UDDAG is also considerably faster than Spade and LapinSpam. Except for extreme cases, UDDAG uses comparable memory to that of PrefixSpan and less memory than Spade and LapinSpam. Additionally, the special feature of UDDAG enables its extension toward applications involving searching in large spaces.

A flexible and efficient sequential pattern mining algorithm

Sequential pattern mining has gathered great attention in recent years due to its broad applications. Most of the existing methods are in two categories: 1) candidate-generation-and-test approaches such as GSP, requiring multiple database scans, 2) pattern-growth approaches such as PrefixSpan, scanning the projected database which may be several times larger than the original database. Methods from both categories must set minimum support thresholds in advance. To remedy the problems, we propose a new approach, Fast Sequential Pattern Enumeration (FSPE), to mine sequential patterns without the need to predetermine the minimum support threshold. The FSPE scans the transaction database only once to enumerate all candidate sequences with efficient indexing of their support counters. Using our approach one can easily produce meaningful rules for any item that appears at least once in the sequence database.

Web Access Pattern Algorithms in Education Domain

Sequential pattern mining discovers frequent user access patterns from web logs. Apriori-like sequential pattern mining techniques requires expensive multiple scans of database. So, now days, WAP (Web Access Pattern) tree based algorithm is used. It is faster than traditional techniques. However, the use of conditional search strategies in WAP-tree based mining algorithms requires re-construction of large numbers of intermediate conditional WAP-trees, which is also very costly.In this paper, Kongu Arts and Science College (KASC) web logs are taken for mining. Here, we propose an efficient sequential pattern mining techniques for KASC web log access sequences known as CS-WAP Tree. This proposed algorithm modifies the WAP tree approach for improving efficiency. The proposed algorithm totally eliminates the need to engage in numerous reconstructions of intermediate WAP trees and considerable reduces execution time. The results of experiments show the efficiency of the improved algorithm. The next key aim is to compare WAP algorithms.

생물학적 데이터 서열들에서 빈번한 최대길이 연속 서열 마이닝

Biological sequences such as DNA sequences and amino acid sequences typically contain a large number of items. They have contiguous sequences that ordinarily consist of hundreds of frequent items. In biological sequences analysis(BSA), a frequent contiguous sequence search is one of the most important operations. Many studies have been done for mining sequential patterns efficiently. Most of the existing methods for mining sequential patterns are based on the Apriori algorithm. In particular, the prefixSpan algorithm is one of the most efficient sequential pattern mining schemes based on the Apriori algorithm. However, since the algorithm expands the sequential patterns from frequent patterns with length-1, it is not suitable for biological dataset with long frequent contiguous sequences. In recent years, the MacosVSpan algorithm was proposed based on the idea of the prefixSpan algorithm to significantly reduce its recursive process. However, the algorithm is still inefficient for mining frequent contiguous sequences from long biological data sequences. In this paper, we propose an efficient method to mine maximal frequent contiguous sequences in large biological data sequences by constructing the spanning tree with the fixed length. To verify the superiority of the proposed method, we perform experiments in various environments. As the result, the experiments show that the proposed method is much more efficient than MacosVSpan in terms of retrieval performance.

Efficient mining of sequential patterns with time constraints: Reducing the combinations

In this paper we consider the problem of discovering sequential patterns by handling time constraints as defined in the G sp algorithm. While sequential patterns could be seen as temporal relationships between facts embedded in the database where considered facts are merely characteristics of individuals or observations of individual behavior, generalized sequential patterns aim to provide the end user with a more flexible handling of the transactions embedded in the database. We thus propose a new efficient algorithm, called G tc ( Graph for Time Constraints) for mining such patterns in very large databases. It is based on the idea that handling time constraints in the earlier stage of the data mining process can be highly beneficial. One of the most significant new feature of our approach is that handling of time constraints can be easily taken into account in traditional levelwise approaches since it is carried out prior to and separately from the counting step of a data sequence. Our test shows that the proposed algorithm performs significantly faster than a state-of-the-art sequence mining algorithm.

Mining Maximal Frequent Contiguous Sequences in Biological Data Sequences

Biological sequences such as DNA and amino acid sequences typically contain a large number of items. They have contiguous sequences that ordinarily consist of more than hundreds of frequent items. In biological sequences analysis(BSA), a frequent contiguous sequence search is one of the most important operations. Many studies have been done for mining sequential patterns efficiently. Most of the existing methods for mining sequential patterns are based on the Apriori algorithm. In particular, the prefixSpan algorithm is one of the most efficient sequential pattern mining schemes based on the Apriori algorithm. However, since the algorithm expands the sequential patterns from frequent patterns with length-1, it is not suitable for biological datasets with long frequent contiguous sequences. In recent years, the MacosVSpan algorithm was proposed based on the idea of the prefixSpan algorithm to significantly reduce its recursive process. However, the algorithm is still inefficient for mining frequent contiguous sequences from long biological data sequences. In this paper, we propose an efficient method to mine maximal frequent contiguous sequences in large biological data sequences by constructing the spanning tree with a fixed length. To verify the superiority of the proposed method, we perform experiments in various environments. The experiments show that the proposed method is much more efficient than MacosVSpan in terms of retrieval performance.

SEQUENTIAL PATTERN MINING AND NONMONOTONIC REASONING FOR INTELLIGENT INFORMATION AGENTS

With the explosive growth of information available on the Internet, more effective data mining and data reasoning mechanism is required to process the sheer volume of information. Belief revision logic offers the expressive power to represent information retrieval contexts, and it also provides a sound inference mechanism to model the nonmonotonicity arising in changing retrieval contexts. Contextual knowledge for information retrieval can be extracted via efficient sequential pattern mining. We present a pattern taxonomy extraction model which efficiently performs the task of discovering descriptive frequent sequential patterns by pruning the noisy associations. This paper illustrates a novel approach of integrating the sequential data mining method into the belief revision based adaptive information agents to improve the agents' learning autonomy and prediction power. Initial experiments show that our belief revision logic and sequential pattern mining based intelligent information agents outperform the vector space model based information agents. Our work opens the door to the development of next generation of intelligent information agents to alleviate the information overload problem.

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.

Multi-Level Linear Location Tree for Efficient Sequential Pattern Mining

Multi-Level Linear Location Tree for Efficient Sequential Pattern Mining

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.

From sequential pattern mining to structured pattern mining: A pattern-growth approach

Sequential pattern mining is an important data mining problem with broad applications. However, it, is also a challenging problem since the mining may have to generate or examine a combinatorially explosive number of intermediate subsequences. Recent studies have developed two major classes of sequential pattern mining methods: (1) a candidate generation-and-test approach, represented by (i) GSP, a horizontal format-based sequential pattern mining method, and (ii) SPADE, a vertical format-based method; and (2) a pattern-growth method, represented by PrefixSpan and its further extensions, such as gSpan for mining structured patterns.In this study, we perform a systematic introduction and presentation of the pattern-growth methodology and study its principles and extensions. We first introduce two interesting pattern-growth algorithms, FreeSpan mid PrefixSpan, for efficient sequential pattern mining. Then we introduce gSpan for mining structured patterns using the same methodology. Their relative performance in large databases is presented and analyzed. Several extensions of these methods are also discussed in the paper, including mining multi-level, multi-dimensional patterns and mining constraint-based patterns.