An Earthquake Sequential Pattern Mining Algorithm Based on General Constraint

Incremental sequential patterns for multivariate temporal association rules mining

The goal of data mining algorithm is to discover useful information embedded in large databases. Frequent itemset mining and sequential pattern mining are two important data mining problems with broad applications. Perhaps the most efficient way to solve these problems sequentially is to apply a pattern-growth algorithm, which is a divide-and-conquer algorithm [9, 10]. In this paper, we present a framework for parallel mining frequent itemsets and sequential patterns based on the divide-and-conquer strategy of pattern growth. Then, we discuss the load balancing problem and introduce a sampling technique, called selective sampling, to address this problem. We implemented parallel versions of both frequent itemsets and sequential pattern mining algorithms following our framework. The experimental results show that our parallel algorithms usually achieve excellent speedups.

This paper proposes a sequential pattern mining (SPM) algorithm in large scale uncertain databases. Uncertain sequence databases are widely used to model inaccurate or imprecise timestamped data in many real applications, where traditional SPM algorithms are inapplicable because of data uncertainty and scalability. In this paper, we develop an efficient approach to manage data uncertainty in SPM and design an iterative MapReduce framework to execute the uncertain SPM algorithm in parallel. We conduct extensive experiments in both synthetic and real uncertain datasets. And the experimental results prove that our algorithm is efficient and scalable.

The rapid evolution of the Internet has resulted in the availability of huge volumes of online learning resources on the web. However, many learners encounter difficulties in retrieval of suitable online learning resources due to information overload. Besides, different learners have different learning needs arising from their differences in learner’s context and sequential access pattern behavior. Traditional recommender systems such as content based and collaborative filtering (CF) use content features and ratings, respectively, to generate recommendations for learners. However, for accurate and personalized recommendation of learning resources, learner’s context and sequential access patterns should be incorporated into the recommender system. Traditional recommendation techniques do not incorporate the learner’s context and sequential access patterns in computing learner similarities and providing recommendations; hence, they are likely to generate inaccurate recommendations. Furthermore, traditional recommender systems provide unreliable recommendations in cases of high rating sparsity. In this paper, we propose a hybrid recommendation approach combining context awareness, sequential pattern mining (SPM) and CF algorithms for recommending learning resources to the learners. In our recommendation approach, context awareness is used to incorporate contextual information about the learner such as knowledge level and learning goals; SPM algorithm is used to mine the web logs and discover the learner’s sequential access patterns; and CF computes predictions and generates recommendations for the target learner based on contextualized data and learner’s sequential access patterns. Evaluation of our proposed hybrid recommendation approach indicated that it can outperform other recommendation methods in terms of quality and accuracy of recommendations.

Sequential pattern mining is to discover frequent sequential patterns in a sequence database. The technique is applied to fields such as web click-stream mining, failure forecast, and traf- fic analysis. Conventional sequential pattern-mining approaches generally focus only the orders of items; however, the time interval between two consecutive events can be a valuable information when the time of the occurrence of an event is concerned. This study extends the concept of the well-known pattern growth approach, PrefixSpan algorithm, to propose a novel sequential pattern mining approach for sequential patterns with time intervals. Unlike the other time-interval sequential pattern-mining algorithms, the approach concerns the time for the next event to occur more than the timing information with its precedent events. To obtain a more reliable sequential pattern, a new measure of the confidence of a sequential pattern is defined. Experiments are conducted to evaluate the performance of the proposed approach.

BackgroundMany studies report about detection and functional characterization of cis-regulatory motifs in untranslated regions (UTRs) of mRNAs but little is known about the nature and functional role of their distribution. To address this issue we have developed a computational approach based on the use of data mining techniques. The idea is that of mining frequent combinations of translation regulatory motifs, since their significant co-occurrences could reveal functional relationships important for the post-transcriptional control of gene expression. The experimentation has been focused on targeted mitochondrial transcripts to elucidate the role of translational control in mitochondrial biogenesis and function.ResultsThe analysis is based on a two-stepped procedure using a sequential pattern mining algorithm. The first step searches for frequent patterns (FPs) of motifs without taking into account their spatial displacement. In the second step, frequent sequential patterns (FSPs) of spaced motifs are generated by taking into account the conservation of spacers between each ordered pair of co-occurring motifs. The algorithm makes no assumption on the relation among motifs and on the number of motifs involved in a pattern. Different FSPs can be found depending on different combinations of two parameters, i.e. the threshold of the minimum percentage of sequences supporting the pattern, and the granularity of spacer discretization. Results can be retrieved at the UTRminer web site: . The discovered FPs of motifs amount to 216 in the overall dataset and to 140 in the human subset. For each FP, the system provides information on the discovered FSPs, if any. A variety of search options help users in browsing the web resource. The list of sequence IDs supporting each pattern can be used for the retrieval of information from the UTRminer database.ConclusionComputational prediction of structural properties of regulatory sequences is not trivial. The presented data mining approach is able to overcome some limits observed in other competitive tools. Preliminary results on UTR sequences from nuclear transcripts targeting mitochondria are promising and lead us to be confident on the effectiveness of the approach for future developments.

Supporting sequential pattern mining from data streams is nowadays a relevant problem in the area of data stream mining research. Actual proposals available in the literature are based on the well-known PrefixSpan approach and are, indeed, able to effectively bound the error of discovered patterns. This approach foresees the idea of dividing the target stream in a collection of manageable chunks, i.e., pieces of stream, in order to gain into effectiveness and efficiency. Unfortunately, mining patterns from stream chunks indeed introduce additional errors with respect to the basic application scenario where the target stream is mined continuously, in a non-batch manner. This is due to several reasons. First, since batches are processed individually, patterns that contain items from two consecutive batches are lost. Secondly, in most batch-based approaches, the decision about the frequency of a pattern is done locally inside a single batch. Thus, if a pattern is frequent in the stream but its items are scattered over different batches, it will be continuously pruned out and will never become frequent due to the algorithm’s lack of the “complete-picture” perspective. In order to address so-delineated pattern mining problems, this paper introduces and experimentally assesses BFSPMiner, a Batch-Free Sequential Pattern Miner algorithm for effectively and efficiently mining patterns in streams without being constrained to the traditional batch-based processing. This allows us, for instance, to discover frequent patterns that would be lost according to alternative batch-based stream mining processing models. We complement our analytical contributions by means of a comprehensive experimental campaign of BFSPMiner against real-world data stream sets and in comparison with current batch-based stream sequential pattern mining algorithms.

Sequential pattern mining is applicable in a wide range of applications since many types of data sets are in a time related format. Besides mining sequential patterns in a single dimension, mining multidimensional sequential patterns can give us more informative and useful patterns. Due to the huge increase in data volume and also quite large search space, efficient solutions for finding patterns in multidimensional sequence data are nowadays very important. For this reason, developing a parallel algorithm is necessary. In this paper, we present a multidimensional sequence model and a parallel algorithm follows the level-wise approach and all participating processors or workers generate candidate sequence and count their supports independently. Simulation experiments show good load balancing and scalable and acceptable speedup over different processors and problem sizes. dense data sets such as telecommunications, where there are many and long frequent patterns, performance of these algorithms degrades incredibly. On the one side, parallel and distributed computing is expected to relieve current mining methods from the sequential bottleneck, providing the ability to scale to massive datasets, and improving the response time. Achieving good performance on today's multiprocessor systems is a non-trivial task. The main challenges include synchronization and communication minimization, work-load balancing, find good data layout and data decomposition, and disk I/O minimization, which is especially important for data mining (4). On the other side, the basic single dimension can not satisfy the requirement of multi-attribute analysis, which is often the case in actual system practice. To address this problem, multidimensional sequence pattern mining is developed. The most time consuming operation in the discovery process of sequential patterns is the computation of the frequency of the occurrences of interesting subsequences in the sequence database. However, the number of sequential patterns grows exponentially and the task of finding all sequential patterns requires a lot of computational resources, which make it an ideal candidate for parallel processing. However, sequential pattern mining suffers from a scalability problem in both memory use and computational time when a dataset size is large. To perform mining on large datasets, we propose to parallelize this task. We demonstrate that our parallel algorithm can effectively alleviate the scalability problem. In this paper, we present a model for multidimensional sequence pattern and then propose a parallel algorithm for mining sequential patterns from multidimensional sequence data. The algorithm is based on the data parallelism strategy of pattern growth. The experimental results show that our parallel algorithms usually achieve good load balancing and scalability.

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.

Mining closed contiguous sequential patterns has been addressed in the literature only recently, through the CCSpan algorithm. CCSpan mines a set of patterns that contains the same information than traditional sets of closed sequential patterns, while being more compact due to the contiguity. Although CCSpan outperforms closed sequential pattern mining algorithms in the general case, it does not scale well on large datasets with long sequences. Moreover, in the context of noisy datasets, the contiguity constraint prevents from mining a relevant result set. Inspired by BIDE, that has proven to be one of the most efficient closed sequential pattern mining algorithm, we propose CCPM that mines closed contiguous sequential patterns, while being scalable. Furthermore, CCPM introduces usable wildcards that address the problem of mining noisy data. Experiments show that CCPM greatly outperforms CCSpan, especially on large datasets with long sequences. In addition, they show that the wildcards allows to efficiently tackle the problem of noisy data.

Sequential pattern is an important research topic in data mining and knowledge discovery. Traditional algorithms for mining sequential patterns are built on the binary attributes databases, which has two limitations. First, it can not concern quantitative attributes; second, only direct sequential patterns are discovered. Mining fuzzy sequential patterns has been proposed to address the first limitation. In this paper, we put forward a discovery algorithm for mining indirect sequential patterns to resolve the second limitation, and a discovery algorithm for mining both direct and indirect fuzzy sequential patterns by combining these two approaches.

From the beginning of sequential pattern mining to the present, this field has received important attention within the data mining area, because it has a wide application in several significant computational problems. Many algorithms have been created and several techniques have been used with the objective of improving the discovery of the frequent sequence set. In this paper we present the main characteristics of some of the most important sequential pattern mining algorithms. Also, we show a comparative performance study among these algorithms.

Uncertainty is inherent in data streams, and present new challenges to data streams mining. For continuous arriving and large size of data streams, modeling sequences of uncertain time series data streams require significantly more space. Therefore, it is important to construct compressed representation for storing uncertain time series data. Based on granules, sequential sketches are created to store hash-compressed granules. And based on sliding windows, a sketch update strategy is given to store most resent granules. As the sequential sketches may be saturated with the increasing of data streams, this paper presents an optimization strategy to delete the absolute sparse patterns. Based on the sequential sketches, a sequential pattern mining algorithm is proposed for mining uncertain data streams. The experimental results illustrate the effectiveness of the pattern mining algorithm.

<abstract><p>Sequential Pattern Mining (SPM) is a branch of data mining that deals with finding statistically relevant regularities of patterns in sequentially ordered data. It has been an active area of research since mid 1990s. Even if many prime algorithms for SPM have a long history, the field is nevertheless very active. The literature is focused on novel challenges and applications, and on the development of more efficient and effective algorithms. In this paper, we present a brief overview on the landscape of algorithms for SPM, including an evaluation on performances for some of them. Further, we explore additional problems that have spanned from SPM. Finally, we evaluate available resources for SPM, and hypothesize on future directions for the field.</p></abstract>

Sequential pattern mining is used to discover temporal relationships between item sets within a large data set. The downside of these approaches is the computation time and memory requirement, which increase exponentially with the data set size. We propose a new algorithm for sequential pattern mining using Apriori-based frequent itemset. In this work, a whole transaction is represented using binary number. The main advantage of the proposed method is in eliminating the necessity to scan the whole data set, for every new set of transactions, which is the limitation in existing sequential pattern mining algorithms. The result of the proposed method is analysed, which shows that the proposed algorithm provides support for large data set analysis, taking care of both execution time and memory usage. Also, we have proposed a pilot approach on how the proposed sequential pattern mining algorithm would work in a parallel environment.