Mining local periodic patterns in a discrete sequence

Discovering periodic frequent travel patterns of individual metro passengers considering different time granularities and station attributes

Periodic pattern mining has become a popular research subject in recent years; this approach involves the discovery of frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic pattern... | Find, read and cite all the research you need on Tech Science Press

Mining productive-periodic frequent patterns in tele-health systems

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.

Mining frequent sequential patterns from sequence databases has been a central research topic in data mining and various efficient mining sequential patterns algorithms have been proposed and studied. Recently, a novel sequential pattern mining research, called mining repetitive gapped subsequences, has attracted the attention of many researchers. However, the number of repetitive gapped subsequences generated by even these closed mining algorithms may be too large to understand for users, especially when support threshold is low. In this paper, we propose the problem of how to compress repetitive gapped sequential patterns. A novel distance measure of repetitive gapped sequential patterns and an efficient representative pattern checking scheme, δ-dominate sequential pattern checking are proposed. We also develop an efficient algorithm, CRGSgrow ( Compressing Repetitive Gapped Sequential pattern grow), including an efficient pruning strategy, SyncScan. An empirical study with both real and synthetic data sets clearly shows that the CRGSgrow has good performance.Keywordsrepetitive gapped sequential patterncompressing frequent patterns

Studying academic behavior patterns is essential for predicting student performance and for devising effective learning interventions. Traditional data mining techniques often do not account for the sequence of academic events and the sequential nature of learning, and therefore are limited in the capacity to discover learning trajectories that make sense. In this paper, an Educational Trajectory Discovery System (ETDS) in education to analyse student academic behavior and utilize the SPADE (Sequential Pattern Discovery using Equivalence classes) algorithm has been proposed for identifying frequent sequential patterns in student activities. The system works with various academic data sources (e.g., learning management system logs, assessments, and attendance and participation data) and organizes the data into event sequences. SPADE finds sequential behavior patterns effectively using vertical id-list databases and equivalence class discovery, which reduces the computational cost by maximizing the benefit of the temporal relationship of the data while retaining its meaning. Event patterns extracted will then be mapped to academic performance outcomes of the learner and categorized into risk-prone, average, or high-achieving academic pathways. This will enable an early assessment of a learner at risk. A visual analytics dashboard improves the interpretability through trajectory visualizations, sequential rule graphs, and trend evolution across semesters. Educators gain actionable intelligence to design specific interventions while learners earn personalized recommendations to improve their learning strategies. Based on simulation results, the proposed system outperforms the standard sequence mining methods in discovery effectiveness, accuracy, and ability to identify at-risk students sooner. The results establish the proposed system as a robust, scalable, data-driven approach to academic monitoring, enabling proactive decision-making and encouraging tailored student learning paths within academic contexts.

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.

Misiurewicz point patterns generation in one-dimensional quadratic maps

Various types of periodic patterns in nucleotide sequences are known to be very abundant in a genomic DNA sequence, and to play important biological roles such as gene expression, genome structural stabilization, and recombination. We present a new method, named "STEPSTONE", to find a specific periodic pattern of repeat sequence, inter-spread repeat, in which the tandem repeats of the conserved and the not-conserved regions appear periodically. In our method, at first, the data on periods of short repeat sequences found in a target sequence are stored as a hash data, and then are selected by application of an auto-correlation test in time series analysis. Among the statistically selected sequences, the inter-spread repeats are obtained by usual alignment procedures through two steps. To test the performance of our method, we examined the inter-spread repeats in Mycobacterium tuberculosis and Zamia paucijuga genomic sequences. As a result, our method exactly detected the repeats in the two sequences, being useful for identifying systematically the inter-spread repeats in DNA sequence.

Periodic pattern mining is the problem that regards temporal regularity. There are many emerging applications in periodic pattern mining, including web usage recommendation, weather prediction, computer networks and biological data. In this paper, we propose a Progressive Timelist-Based Verification (PTV) method to the mining of periodic patterns from a sequence of event sets. The parameter min_rep, is employed to specify the minimum number of repetitions required for a valid segment of non-disrupted pattern occurrences. We also describe a partitioning approach to handle extra large/long data sequence. The experiments demonstrate good performance and scalability with large frequent patterns.

BackgroundAnimals, including humans, exhibit a variety of biological rhythms. This article describes a method for the detection and simultaneous comparison of multiple nycthemeral rhythms.MethodsA statistical method for detecting periodic patterns in time-related data via harmonic regression is described. The method is particularly capable of detecting nycthemeral rhythms in medical data. Additionally a method for simultaneously comparing two or more periodic patterns is described, which derives from the analysis of variance (ANOVA). This method statistically confirms or rejects equality of periodic patterns. Mathematical descriptions of the detecting method and the comparing method are displayed.ResultsNycthemeral rhythms of incidents of bodily harm in Middle Franconia are analyzed in order to demonstrate both methods. Every day of the week showed a significant nycthemeral rhythm of bodily harm. These seven patterns of the week were compared to each other revealing only two different nycthemeral rhythms, one for Friday and Saturday and one for the other weekdays.

Discovery of sequential patterns is becoming increasingly useful and essential in many scientific and commercial domains. Enormous sizes of available datasets and possibly large number of mined patterns demand efficient and scalable algorithms. In this paper we present two parallel formulations of a serial sequential pattern discovery algorithm based on tree projection that are well suited for distributed memory parallel computers. Our experimental evaluation on a 32 processor IBM SP show that these algorithms are capable of achieving good speedups, substantially reducing the amount of the required work to find sequential patterns in large databases.

Mining periodic high-utility itemsets with both positive and negative utilities

GENERAL COMMENTARY article Front. Comput. Neurosci., 12 August 2013 https://doi.org/10.3389/fncom.2013.00106

Advanced satellite tracking technologies enable biologists to track animal movements at finer spatial and temporal scales. The resulting long-term movement data is very meaningful for understanding animal activities. Periodic pattern analysis can provide insightful approach to reveal animal activity patterns. However, individual GPS data is usually incomplete and in limited lifespan. In addition, individual periodic behaviors are inherently complicated with many uncertainties. In this paper, we address the problem of mining periodic patterns of animal movements by combining multiple individuals with similar periodicities. We formally define the problem of mining common periodicity and propose a novel periodicity measure. We introduce the information entropy in the proposed measure to detect common period. Data incompleteness, noises, and ambiguity of individual periodicity are considered in our method. Furthermore, we mine multiple common periodic patterns by grouping periodic segments w.r.t. the detected period, and provide a visualization method of common periodic patterns by designing a cyclical filled line chart. To assess effectiveness of our proposed method, we provide an experimental study using a real GPS dataset collected on 29 birds in Qinghai Lake, China.