High Utility Periodic Frequent Pattern Mining in Multiple Sequences

Mining local periodic patterns in a discrete 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.

Effective periodic pattern mining in time series databases

A spatio-temporal trajectory captures the movement behaviors of an object, and reveals various periodic patterns for the object such as where and when the object regularly visits. Due to the recent advances in GPS-enabled data collection devices such as mobile phones, a large set of spatio-temporal trajectories has been collected and available for analysis. These spatio-temporal trajectories could be used to identify those people who periodically visit medical centres for treatments (patients), working (health professionals) or other purposes. Spatio-temporal periodic pattern mining is to find periodic patterns for a certain place at regular intervals from spatio-temporal trajectories. Past studies attempt to find periodic patterns in medical contexts through time-series datasets, but not from spatio-temporal trajectories. In this study, we introduce a medical periodic pattern mining framework that utilises spatio-temporal periodic pattern mining approaches to find medical periodic patterns. We test the feasibility and applicability of our framework through a real-world publicly available dataset. Experimental results reveal that our framework is able to identify those people who regularly visit medical centres from those not, and also find medical periodic patterns revealing interesting medical behaviors.

Periodic patterns are mined individually on structural and weight aspects of an interaction in a dynamic network. However, these interactions possess a direction aspect too. Moreover, some applications require patterns on both aspects i) on direction and ii) on weight of directed interactions for a better understanding of their behaviour. To the authors' knowledge, no such work is available that mines both types of periodic patterns in a single run. To overcome this limitation, the authors propose a framework to mine periodic patterns on both the aspects. The framework first mines periodic patterns on direction, and then only the edges present in the patterns obtained are considered further for patterns on weight of directed interactions. Further, the patterns are being analysed to develop a better understanding of the dynamic network. To do so, a set of six parameters explained later in the text is proposed to study the behaviour of interactions at microscopic level. The framework is tested on real world and synthetic datasets. The results highlight its practical scalability and prove its efficiency.

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

Hierarchical trajectory clustering for spatio-temporal periodic pattern mining

Events are ubiquitous in real-life. With the rapid rise of the popularity of social media channels, massive amounts of event data, such as information about festivals, concerts, or meetings, are increasingly created and shared by users on the Internet. Deriving insights or knowledge from such social media data provides a semantically rich basis for many applications, for instance, social media marketing, service recommendation, sales promotion, or enrichment of existing data sources. In spite of substantial research on discovering valuable knowledge from various types of social media data such as microblog data, check-in data, or GPS trajectories, interestingly there has been only little work on mining event data for useful patterns.
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\nIn this thesis, we focus on the discovery of interesting, useful patterns from datasets of events, where information about these events is shared by and spread across social media platforms. To deal with the existence of heterogeneous event data sources, we propose a comprehensive framework to model events for pattern mining purposes, where each event is described by three components: context, time, and location. This framework allows one to easily define how events are related in terms of conceptual, temporal, and spatial (geographic) relationships. Moreover, we also take into account hierarchies for contexts, time, and locations of events, which naturally exist as useful background knowledge to derive patterns at different levels of abstraction and granularity. Based on this framework, we focus on the following problems: (i) mining interval-based event sequence patterns, (ii) mining periodic event patterns, and (iii) extracting semantic annotations for locations of events. Generally, the first two problems consider correlations of events whereas the last one takes correlations of event components into account.
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\nIn particular, the first problem is a generalization of mining sequential patterns from traditional data, where patterns representing complex temporal relationships among events can be discovered at different levels of abstraction and granularity. The second problem is to find periodic event patterns, where a notion of relaxed periodicity is formulated for events as well as for groups of events that co-occur. The third~problem is to extract semantic annotations for locations on the basis of exploiting correlations of contexts, time, and locations of events. For the three problems above, we respectively propose novel and efficient approaches. Our experiments clearly indicate that extracted patterns and knowledge can be well utilized in various useful tasks, such as event prediction, semantic search for locations, or topic-based clustering of locations.

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.

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

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.

As a new paradigm of information technology, the Internet of Things (IoT) is attracting increasing attention from various industrial fields. It is foreseeable that the applications of IoT will be prevalent in the public transportation system and bring changes to the system in the near future. In this paper, we analyze the impact of IoT environment on the public transportation system, propose a new framework of the intelligent public transportation system based on IoT, and present the deployment of the elements, the communication network, and the three-tier architecture of the system in detail. We also present the information flow, technical scheme, optimization model, and algorithm of the main modules of dynamic optimization of the system. The innovative points of this paper lie in: (1) a new framework for public transport system based on IoT, which integrates the scheduling problems of subway, bus, and shared taxi, is proposed for better-coordinated transfer solutions; (2) transport flow prediction methods based on periodic patterns mining is proposed for road flow analysis and passenger flow analysis, and; (3) mathematical model and DSS-based evolutionary computation algorithm are proposed for solving the dynamic bus scheduling and controlling problems. The proposed intelligent transport system based on IoT can assist the decision makers to increase the utilization rate of the transport resources, improve the efficiency of scheduling, and reduce passengers' traveling time.

International audience

Graph is one of the media to represent and summarise interactions in a time varying network. Often, interactions repeat after a fixed interval of time and exhibit temporal periodicity. Existing algorithms focus either on the structure or on the weight of periodic interactions individually. But, for instance, a stock analyst requires evidence of both structure and weight (here, price) of the stock pairs to make prediction and discovers information about the profit producing stocks and the actual profit. On performing experiments using existing algorithms explicitly, it is observed that the efficiency is lost in such applications. Hence, in this paper, we provide an efficient framework based on available algorithms to mine periodic patterns both on structure and weight in a weighted dynamic network. The proposed framework consists of a mapping between interactions that are periodic on structure and weight. We have performed experiments on synthetic and real world datasets. The results validate the scalability and practical feasibility of the proposed framework.

As an important branch of data mining, sequential patterns mining has been extensively studied. Based on sequential patterns mining, Structural Relation Patterns (SRPs) mining is proposed for mining relations among sequences, these relations are generally hidden behind sequential patterns. Upon the previous researches, the concepts of concurrent relation pattern and exclusive relation pattern are redefined; the definitions of ordered relation pattern and iterate relation pattern are given. The properties of SRPs are discussed, and they form a theoretical foundation for further study of structural relation patterns and relative mining algorithms. Beside, the thinking of mining associate relations among sequential patterns is proposed. SRPs mining is significant in practical applications same as sequential patterns mining.