From Sequential Patterns to Structural Relation Patterns

Structural Relation Patterns (SRPs) mining is proposed for mining relations among sequences, these relations are generally hidden behind sequential patterns. Concurrent Sequential Pattern (CSP) and Exclusive Sequential Pattern (ESP) are two important parts of SRP, called Structural Relation Sequential Pattern (SRSP). Upon the previous researches, the concepts of SRSP are redefined; the properties of SRSP are discussed; the algorithms for mining SRSPs are studied. All of these form a theoretical foundation for further study of structural relation patterns and relative mining algorithms. SRSPs mining is significant in practical applications same as sequential patterns mining.

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.

Incremental sequential patterns for multivariate temporal association rules mining

Modeling human behavior on the Web is an essential goal of Web usage mining and is often performed by sequential pattern mining (SPM). The similarity between data elements and the variability of human behavior often result in the decrease of the support value of some patterns and thus limit the number of patterns mined. This is often associated with a loss of information. As the data can take the form of multiple data sources that represent multiple views of the area of interest, they can be used to handle this similarity and variability. Traditional approaches form a unique dataset from these data. However, the associated mining task is complex and generates a large and redundant set of sequential patterns. This work proposes G_SPM, a pattern mining algorithm designed for behavioral pattern mining that takes advantage of multi-source data to handle the similarity, while adopting a selective mining strategy to limit the complexity of the mining process and the increase in the number of patterns. It considers the behavioral data source as the main source, and exploits complementary sources only when similarity is suspected. It forms frequent general patterns that represent sets of similar behavioral patterns with a limited frequency, while controlling the level of generality. Experimental results confirm that G_SPM succeeds in mining general patterns and thus in handling the problem of item similarity. In addition G_SPM outperforms traditional approaches in terms of runtime and redundancy of the resulting set of patterns.

In this paper the problem of Contiguous Item Sequential Pattern (CISP) Mining is presented as a sequential pattern mining problem under two constraints. First, each element in a sequence consists of only one item. Second, items appearing in the sequences that contain a pattern must be adjacent with respect to the underlying order as they appear in the pattern. Even though the problem of CISP mining can be solved by using previous approaches on sequential pattern mining under a general constraint description framework, this may lead to poor performance due to the large searching space. To efficiently solve this problem, a new data structure, UpDown Tree, is proposed for CISP mining. UpDown Tree based approach can greatly improve the efficiency of CISP mining in terms of both time and memory comparing to previous approaches. An extensive experimental study has shown promising results with our approach.

Sequential pattern mining is an important mining task for discovering sequential patterns along with their insight relationships in many real-world applications. In practice, sequence databases are kept changing over the time along with their business. For some reasons, some sequences in the database are asked to be deleted from the database. In order to have a synchronization of discovered sequential patterns with the database from which they have been discovered, the sequential pattern mining task is re-considered with many challenges. As the number of deleted sequences is often smaller than the size of the entire database, re-mining from scratch the updated database might incur a high cost because sequential pattern mining is a computationally expensive task. In this paper, our work aims at an efficient incremental mining solution to the sequential pattern mining task with sequence deletions. Different from the existing works, we propose an expanded prefix tree by extending the existing prefix tree with additional structures for capturing more necessary information for the incremental mining process. Based on this tree, we propose an incremental sequential pattern mining algorithm, SPMD, for finding a complete set of sequential patterns with no re-scanning the original database, when a number of sequences in the database are deleted. Experimental results on the benchmark databases have confirmed that our SPMD algorithm outperforms the re-mining from scratch with the PrefixSpan algorithm with less running time.

Data mining has been defined as the non- trivial extraction of implicit, previously unknown and potentially useful information from data. Association mining and sequential mining analysis are considered as crucial components of strategic control over a broad variety of disciplines in business, science and engineering. Association mining is one of the important sub-fields in data mining, where rules that imply certain association relationships among a set of items in a transaction database are discovered. In Sequence mining, data are represented as sequences of events, where order of those events is important. Finding patterns in sequences is valuable for predicting future events. In many applications such as the WEB applications, stock market, and genetic analysis, finding patterns in a sequence of elements or events, helps in predicting what could be the next event or element. At the conceptual level, association mining and sequence mining are two similar processes but using different representations of data. In association mining, items are distinct and the order of items in a transaction is not important. While in sequential pattern mining, the order of elements (events) in transactions (sequences) is important, and the same event may occur more than once. In this paper, we propose a new mapping function that maps event sequences into itemsets. Based on the unified representation of the association mining and the sequential pattern, a new approach that uses the Boolean representation of input database D to build a Boolean matrix M. Boolean algebra operations are applied on M to generate all frequent itemsets. Finally, frequent items or frequent sequential patterns are represented by logical expressions that could be minimized by using a suitable logical function minimization technique.

In this paper we propose a sequential pattern mining method to analyze multimodal data streams using a quantitative temporal approach. While the existing algorithms can only find sequential orders of temporal events, this paper presents a new temporal data mining method focusing on extracting exact timings and durations of sequential patterns extracted from multiple temporal event streams. We present our method with its application to the detection and extraction of human sequential behavioral patterns over multiple multimodal data streams in human-robot interactions. Experimental results confirmed the feasibility and quality of our proposed pattern mining algorithm, and suggested a quantitative data-driven way to ground social interactions in a manner that has never been achieved before.

Targeted mining of contiguous sequential patterns

Sequential pattern mining is used to find frequent data sequences over time. When sequential patterns are generated, the newly arriving patterns may not be identified as frequent sequential patterns due to the existence of old data and sequences. Progressive sequential pattern mining aims to find the most up-to-date sequential patterns given that obsolete items will be deleted from the sequences. When sequences come with multiple data streams, it is difficult to maintain and update the current sequential patterns. Even worse, when we consider the sequences across multiple streams, previous methods cannot efficiently compute the frequent sequential patterns. In this work, we propose an efficient algorithm PSP-AMS to address this problem. PSP-AMS uses a novel data structure PSP-MS-tree to insert new items, update current items, and delete obsolete items. By maintaining a PSP-MS-tree, PSP-AMS efficiently finds the frequent sequential patterns across multiple streams. The experimental results show that PSP-AMS significantly outperforms previous algorithms for mining of progressive sequential patterns across multiple streams on synthetic data as well as real data.

In this paper, we present a new algorithm, Weighted Sequential support affinity pattern mining in which a new measure, sequential s-confidence is suggested. By using the measure, sequential patterns with support affinity are generated. A comprehensive performance study shows that WSAffinity is efficient and scalable in weighted sequential pattern mining. Moreover, it generates fewer but important sequential patterns.Keywordsdata miningsequential pattern miningsupport affinity pattern

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.

Text mining used on texts and publications in the biomedical and molecular biology fields is referred to as "biomedical text mining." It is a relatively new area of study at the intersection of computational linguistics, bioinformatics, and natural language processing. Superior usefulness the goal of sequential pattern mining is to identify statistically significant patterns among data instances when the values are presented sequentially. Time series mining is typically regarded as a distinct activity even if it is closely linked since it is typically assumed that the values are discrete. Structured data mining has a unique use known as sequential pattern mining. High utility pattern (HUP) mining is one of the most relevant study areas in data mining nowadays since it is capable of taking into consideration the nonbinary frequency values of items in transactions as well as different profit values for each item. The utilization of previous data structures as well as mining outcomes, yet, enables incremental and interactive data mining to eliminate the need for further calculations when a database is updated or the minimum threshold is modified. The method in this study suggests three innovative tree architectures for effective incremental and interactive HUP mining. The high utility sequential pattern mining issue has formalised key ideas and elements.

One of the sub areas of the data mining includes sequential pattern mining. This mining algorithm is to find the repeating patterns after mining the sequence databases. These are used to find the relation between the various items in the data for different purposes. As these data keep changing according to the change in time, mining should be done on incremented or updated database to obtain the frequent sequential patterns. The proposed algorithm in this paper uses modified algorithm of sequential pattern mining including concepts of fuzzy space intervals. In this algorithm, frequently occurring sequential patterns in the sequence database are mined using apriori like method. Fuzzy theory is used for mining the space interval between the frequently occurring sequences. The sequentially occurring candidate patterns are found first. After that follows the frequently occurring sequential patterns, which are found by calculating the minimum fuzzy support along with the use of the fuzzy number. Each space cluster is found by fuzzy support computation. The final results comprises the frequently occurring fuzzy space sequentially based patterns. At last the outcome also confirms the excellence of the suggested MISPFSI algorithm.