Periodic Pattern Mining Research Articles

An Approach for Seasonally Periodic Frequent Pattern Mining in Retail Supermarket

Periodic-Frequent pattern mining is an empirical data mining technique that is used to investigate the customer purchase behavior pattern in the retail market. It provides the most extreme advantages to a retailer for product placements, product segmentation, store layout, selling strategies and different merchandising decisions to improve customer satisfaction as well as increase the sales. Traditionally, the single minimum support based frequent pattern mining approaches missed many lower supported items for high support threshold value, and on the other sites, the number of frequent patterns explode for low support threshold value. The retail market is highly seasonal, a number of items those are not periodic-frequent at all but those are periodic and frequent at a specific season, as results a huge number of seasonal periodic-frequent itemsets are remains out of reach. To resolve this problem, we propose an approach for extracting seasonally periodic-frequent patterns with user specified minimum support and maximum periodicity thresholds on multilevel retail supermarket dataset in this paper. The dataset has been created by collecting transaction slips in a Supermarket of Bangladesh. The results of our proposed approach extract many hidden seasonal patterns in transactional database for easy merchandising decisions and it shows the superiority over the traditional systems.

Periodicity-Oriented Data Analytics on Time-Series Data for Intelligence System

Periodic pattern mining models analyze patterns which occur periodically in a time-series database, such as sensor readings of smartphones and/or Internet of Things devices. The extracted patterns can be utilized for risk prediction, system management, and decision-making. In this article, we propose an efficient periodicity-oriented data analytics approach. It ignores intermediate events deliberately by adopting the concept of flexible periodic patterns, so it can be applied to more diverse real-life scenarios and systems. Moreover, the proposed approach adopts a novel symbol-centered data structure instead of existing data structures for state-of-the-art approaches of periodic pattern mining. Performance evaluations on real-life datasets, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Diabetes, Oil Prices</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bike Sharing</i> , and requirements show that our approach has better runtime, memory usage, number of visited patterns, and sensitivity than efficient periodic pattern mining (EPPM) and flexible periodic pattern mining (FPPM), which are the state-of-the-art approaches in the same field. The experimental results show that the proposed algorithm will require less runtime and smaller memory than the existing algorithms on most data and requirements in real life.

Mining local periodic patterns in a discrete sequence

Periodic frequent patterns are sets of events or items that periodically appear in a sequence of events or transactions. Many algorithms have been designed to identify periodic frequent patterns in data. However, most assume that the periodic behavior of a pattern does not change much over time. To address this limitation, this paper proposes to discover a novel type of periodic patterns in a sequence of events or transactions, called Local Periodic Patterns (LPPs) which are patterns (sets of events) that have a periodic behavior in some non predefined time-intervals. A pattern is said to be a local periodic pattern if it appears regularly and continuously in some time-interval(s). Two novel measures are proposed to assess the periodicity and frequency of patterns in time-intervals. The maxSoPer (maximal period of spillovers) measure allows detecting time-intervals of variable lengths where a pattern is continuously periodic, while the minDur (minimal duration) measure ensures that those time-intervals have a minimum duration. To discover all LPPs, the paper presents three efficient algorithms. An experimental evaluation on real datasets shows that the proposed algorithms are efficient and can provide useful patterns that cannot be found using traditional periodic pattern mining algorithms.

Mining Evolution Patterns from Complex Trajectory Structures—A Case Study of Mesoscale Eddies in the South China Sea

Real-word phenomena, such as ocean eddies and clouds, tend to split and merge while they are moving around within a space. Their trajectories usually bear one or more branches and are accordingly defined as complex trajectories in this study. The trajectories may show significant spatiotemporal variations in terms of their structures and some of them may be more prominent than the others. The identification of prominent structures in the complex trajectories of such real-world phenomena could better reveal their evolution processes and even shed new light on the driving factors behind them. Methods have been proposed for the extraction of periodic patterns from simple trajectories (i.e., those with linear structure and without any branches) with a focus on mining the related temporal, spatial or semantic information. Unfortunately, it is not appropriate to directly use such methods to examine complex trajectories. This study proposes a novel method to study the periodic patterns of complex trajectories by considering the inherent spatial, temporal and topological information. First, we use a sequence of symbols to represent the various structures of a complex trajectory over its lifespan. We then, on the basis of the PrefixSpan algorithm, propose a periodic pattern mining of structural evolution (PPSE) algorithm and use it to identify the largest and most frequent patterns (LFPs) from the symbol sequence. We also identify potential periodic behaviors. The PPSE method is then used to examine the complex trajectories of the mesoscale eddy in the South China Sea (SCS) from 1993 to 2016. The complex trajectories of ocean eddies in the southeast of Vietnam show are different from other regions in the SCS in terms of their structural evolution processes, as indicated by the LFPs with the longest lifespan, the widest active range, the highest complexity, and the most active behaviors. The LFP in the southeast of Vietnam has the longest lifespan, the widest active range, the highest complexity, and the most active behaviors. Across the SCS, we found seven migration channels. The LFPs of the eddies that migrate through these channels have a temporal cycle of 17–24 years. These channels are also the regions where eddies frequently emerge, as revealed by flow field data.

A comprehensive survey on trajectory-based location prediction

Recent explorative growth in telecommunication and telepathy technology has flooded the market with location-based data, which paves the way for location-aware prediction services. These applications have vast domain influence in route navigation, recommendation system, traffic-congestion control, ecological study, climatological forecast, and many more. Research efforts are spent on the put-forward overall picture of location prediction through trajectory data. This survey offers an extensive overview of location prediction enveloping basic definitions and concepts, data sources, approaches, and applications. Moreover, Spatial–Temporal pattern-based prediction models are discussed, highlighting the advantages and disadvantages of each. Sequential, periodic, and frequent pattern mining advances are noted. This paper presents a recent deep learning methodology for extracting features of a large trajectory. Distributive big data models using Hadoop and MapReduce frameworks are recorded. Location prediction using the social media platform is mentioned. Content-based and semantic mining models are studied. Tables and diagrams are displayed to provide at a glance view to facilitate smooth understanding. Furthermore, application and challenges are addressed related to the next location prediction. The overall conclusion of the survey and future directions are also listed.

Finding spectrum occupancy pattern using CBFPP mining technique

The main challenge of problem lies in the perception of Cognitive Radio technology is to discover licensed empty spectrum pattern. The efficient model is needed for allocation among licensed and unlicensed users in wireless spectrum to improve the extraction rate and collision rate. To discover the spectrum hole in spectrum paging bands, stirred by FP mining technique proposed an efficient enumeration approach, namely Constraint Based Frequent Periodic Pattern Mining (CBFPP). The proposed algorithm uses TRIE-like data structure with data mining constraints. CBFPP algorithm predicts periodic spectrum occupancy holes in the paging bands. It is shown that CBFPP has a high prediction accuracy with reasonable time complexity. Experiment with synthetic and real data validate higher prediction accuracy and with reasonable time complexities. The unlicensed user utilizes the predicted spectrum pattern in spectrum usage of channel without significant interference to licensed users.

APDS: A framework for discovering movement pattern from trajectory database

Currently, the boosting of location acquisition devices makes it possible to track all kinds of moving objects, and collect and store their trajectories in database. Therefore, how to find knowledge from huge amount of trajectory data has become an attractive topic. Movement pattern is an efficient way to understand moving objects’ behavior and analyze their habits. To promote the application of spatiotemporal data mining, a moving object activity pattern discovery system is designed and implemented in this article. First of all, raw trajectory data are preprocessed using methods like data clean, data interpolation, and compression. Second, a simplified density-based trajectory clustering algorithm is implemented to find and group similar movement patterns. Third, in order to discover the trends and periodicity of movement pattern, a trajectory periodic pattern mining algorithm is developed. Finally, comprehensive experiments with different parameters are conducted to validate the pattern discovery system. The experimental results show that the system is robust and efficient to analyze moving object trajectory data and discover useful patterns.

Towards fast and memory efficient discovery of periodic frequent patterns

ABSTRACTPeriodic frequent pattern (PFP) mining, the process of discovering frequent patterns that occur at regular periods in databases, is an important data mining task for various decision-making. Although several algorithms have been proposed for discovering PFPs, most of these algorithms often employ a two-stage approach to mining these periodic frequent patterns. That is, by firstly deriving the set of periods of a pattern from its coverset and subsequently evaluating the patterns' periodicity from the derived set of periods. This two-stage approach in discovering periodic frequent patterns as a result make existing algorithms inefficient in both runtime and memory usage. This paper presents solutions towards reducing the runtime, as well as, memory usage in discovering periodic frequent patterns. This is achieved by evaluating the periodicity of patterns without deriving the set of periods from their coversets. Experimental analysis on benchmark datasets show that the proposed solutions are efficient in reducing both the runtime and memory usage in mining periodic frequent patterns.

Semantic periodic pattern mining from spatio-temporal trajectories

Rapid development in GPS tracking techniques produces a large number of spatio-temporal trajectory data. The analysis of these data provides us with a new opportunity to discover behavioural patterns. Spatio-temporal periodic pattern mining is finding temporal regularities for interesting places. Mining periodic patterns from spatio-temporal trajectories reveals useful and important information about people’s regular and recurrent movements and behaviours. Existing periodic pattern mining algorithms suffer from two main drawbacks. They assume regularly sampled and evenly spaced trajectory data as input which is unlike real world data, traditional methods also fail to consider background aspatial information despite many applications requiring a semantic interpretation of movement behaviours. In this paper, we propose a new semantic periodic pattern mining algorithm from spatio-temporal trajectories that overcomes these two drawbacks from past studies. Experimental results with real world datasets demonstrate the efficiency and effectiveness of our proposed method.

Discovering Periodic Itemsets Using Novel Periodicity Measures

Discovering periodic patterns in a customer transaction database is the task of identifying itemsets (sets of items or values) that periodically appear in a sequence of transactions. Numerous methods can identify patterns exhibiting a periodic behavior. Nonetheless, a problem of these traditional approaches is that the concept of periodic behavior is defined very strictly. Indeed, a pattern is considered to be periodic if the amount of time or number of transactions between all pairs of its consecutive occurrences is less than a fixed maxPer (maximum periodicity) threshold. As a result, a pattern can be eliminated by a traditional algorithm for mining periodic patterns even if all of its periods but one respect the maxPer constraint. Consequently, many patterns that are almost always periodic are not presented to the user. But these patterns could be considered as interesting as they generally appear periodically. To address this issue, this paper suggests to use three measures to identify periodic patterns. These measures are named average, maximum and minimum periodicity, respectively. They are each designed to evaluate a different aspect of the periodic behavior of patterns. By using them together in a novel algorithm called Periodic Frequent Pattern Miner, more flexibility is given to users to select patterns meeting specific periodic requirements. The designed algorithm has been evaluated on several datasets. Results show that the proposed solution is scalable, efficient, and can identify a small sets of patterns compared to the Eclat algorithm for mining all frequent patterns in a database.

Efficient algorithms to identify periodic patterns in multiple sequences

Periodic pattern mining is a popular data mining task, which consists of identifying patterns that periodically appear in data. Traditional periodic pattern mining algorithms are designed to find patterns in a single sequence. However, in several domains, it is desirable to discover patterns that are periodic in many sequences. An example of such application is market basket analysis. Given a database of sequences of transactions made by customers, discovering sets of items that are periodically bought by customers can help understand customer behavior. To discover periodic patterns common to multiple sequences, this paper extends the traditional problem of mining periodic patterns in a sequence. Two novel measures are defined called the standard deviation of periods and the sequence periodic ratio. Two algorithms are proposed to mine these patterns efficiently called MPFPSBFS and MPFPSDFS, which perform a breadth-first search and depth-first search, respectively. Because the sequence periodic ratio is neither monotone nor anti-monotone, these algorithms rely on a novel upper-bound called boundRa and two novel search space pruning properties to find periodic patterns efficiently. The algorithms have been evaluated on multiple datasets. Results show that they are efficient and can filter numerous non periodic itemsets to identify periodic patterns.

Context-Enriched Regular Human Behavioral Pattern Detection From Body Sensors Data

Extracting indicative characteristics from the sensor data provide diverse avenues for improving the well-being of the elderly people living alone in their homes through understanding and identifying their behavioral patterns while considering any environmental changes. In this paper, we present a new model to explore the challenges associated with mining patterns from the body sensor data and their potential use in discovering regular human routines through mining periodic patterns from a non-uniform temporal database. The non-uniform nature of the temporal database adds more challenges to the mining of periodic patterns as the items may have different periodicity and frequency occurrences. Another challenge is how to discover the correlation between the discovered patterns. In addition, we examine the context-enriched periodic patterns which provide more insights about residents’ health. A new algorithm for the contextualized-correlated periodic pattern mining from a non-uniform temporal database is presented along with an extensive evaluation of its performance using a real-life dataset.

A New Framework of Intelligent Public Transportation System Based on the Internet of Things

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.

Mining hierarchical semantic periodic patterns from GPS-collected spatio-temporal trajectories

A large number of spatio-temporal trajectory data is being generated from GPS enabled devices such as cars, smartphones, and sensors. These trajectory datasets representing objects’ movements provide new opportunities for enhanced spatio-temporal periodic pattern mining. These GPS collected trajectory datasets represent real-world movement phenomena and thus they are spatially placed, temporally recorded, aspatial semantically meaningful, hierarchically structured, and irregularly sampled. Periodic pattern mining from spatio-temporal trajectories is to find temporal regularities from these spatio-temporal trajectories, and thus must take into these five characterisics into account in order not to miss any spatio-temporally, semantically and hierarchically meaningful patterns from irregularly sampled spatio-temporal trajectories. Traditional periodic pattern mining fails to consider these five conditions simultaneously, and in this paper, we propose a hierarchical clustering based semantic periodic pattern mining to consider the five aspects: spatiality, temporality, semantics, hierarchy, and irregularity. Experimental results demonstrate the effectiveness of our proposed method against traditional periodic pattern mining approaches.

An innovative model to mine asynchronous periodic pattern of moving objects

Periodic detection in spatiotemporal data is one of the research focuses in data mining. Most previous works only focused on mining periodic patterns and hardly recognized misaligned presence of a pattern due to the intervention of repetitive data. A more flexible asynchronous periodic patterns mining model (AP2M2) based on clustering algorithm and SMCA algorithm is proposed which mainly has three steps, discovering the invisible repetitive data and clustering them into a single record to generate standard and usable dataset, finding the set of stopovers and mining the periodic patterns of moving objects at each stopover. In the experiment, the Chinese bird-watching data is used to check the effectiveness of AP2M2 model and the results show that the AP2M2 model can precisely mine the asynchronous periodic patterns with low time complexity.

An efficient algorithm for mining periodic high-utility sequential patterns

A periodic high-utility sequential pattern (PHUSP) is a pattern that not only yields a high-utility (e.g. high profit) but also appears regularly in a sequence database. Finding PHUSPs is useful for several applications such as market basket analysis, where it can reveal recurring and profitable customer behavior. Although discovering PHUSPs is desirable, it is computationally difficult. To discover PHUSPs efficiently, this paper proposes a structure for periodic high-utility sequential pattern mining (PHUSPM) named PUSP. Furthermore, to reduce the search space and speed up PHUSPM, a pruning strategy is developed. This results in an efficient algorithm called periodic high-utility sequential pattern optimal miner (PUSOM). An experimental evaluation was performed on both synthetic and real-life datasets to compare the performance of PUSOM with state-of-the-art PHUSPM algorithms in terms of execution time, memory usage and scalability. Experimental results show that the PUSOM algorithm can efficiently discover the complete set of PHUSPs. Moreover, it outperforms the other four algorithms as the former can prune many unpromising patterns using its designed structure and pruning strategy.

Mining non-redundant closed flexible periodic patterns

Mining periodic patterns from time series databases is needed to predict any future situation. Flexible pattern mining is a special kind of periodic pattern mining where intermediate events can be overlooked purposely. Mining such patterns from time series data is advantageous due to its capability of modeling various real life scenarios. The goal of mining closed flexible patterns is to avoid unnecessary flexible patterns but preserving the same information of a complete set of patterns. Though it has wide range of application domains, existing algorithms failed to mine closed flexible patterns without generating any false positive, i.e. non-closed and ∕or redundant patterns. In this paper, a new algorithm NRCFP (Non-Redundant Closed Flexible Pattern) has been proposed that generates complete set of non-redundant closed flexible patterns in time series databases. Three pruning techniques- BackScan (existing), RangeScan (proposed) and Column-pruning (proposed) have been applied to avoid generation of non-closed patterns, redundant flexible patterns and fictitious patterns. Proposed NRCFP efficiently mines non-redundant closed flexible periodic patterns. The performance of our algorithm has been extensively analyzed using several real-life databases based on runtime and memory consumption and compared with existing state-of-the-arts approach to prove effectiveness of the algorithm with respect to required processing time and memory consumption. Some applications of our proposed algorithm in various real life domains are discussed.

Hierarchical trajectory clustering for spatio-temporal periodic pattern mining

Spatio-temporal periodic pattern mining is to find temporal regularities for interesting places. Many real world spatio-temporal phenomena present sequential and hierarchical nature. However, traditional spatio-temporal periodic pattern mining ignores the consideration of sequence, and fails to take into account inherent hierarchy. This paper proposes a hierarchical trajectory clustering based periodic pattern mining that overcomes the two common drawbacks from traditional approaches: hierarchical reference spots and consideration of sequence. We propose a new trajectory clustering algorithm which considers semantic spatio-temporal information such as direction, speed and time based on Traclus and present comparative experimental results with three popular clustering methods: Kernel function, Grid-based, and Traclus. We further extend the proposed trajectory clustering to hierarchical clustering with the use of the single linkage approach to generate a hierarchy of reference spots. Experimental results reveal various hierarchical periodic patterns, and demonstrate that our algorithm outperforms traditional reference spot detection algorithms.

A new framework for mining weighted periodic patterns in time series databases

Mining periodic patterns in time series databases is a daunting research task that plays a significant role at decision making in real life applications. There are many algorithms for mining periodic patterns in time series, where all patterns are considered as uniformly same. However, in real life applications, such as market basket analysis, gene analysis and network fault experiment, different types of items are found with several levels of importance. Again, the existing algorithms generate huge periodic patterns in dense databases or in low minimum support, where most of the patterns are not important enough to participate in decision making. Hence, a pruning mechanism is essential to reduce these unimportant patterns. As a purpose of mining only important patterns in a minimal time period, we propose a weight based framework by assigning different weights to different items. Moreover, we develop a novel algorithm, WPPM (Weighted Periodic Pattern Mining Algorithm), in time series databases underlying suffix trie structure. To the best of our knowledge, ours is the first proposal that can mine three types of weighted periodic pattern, (i.e. single, partial, full) in a single run. A pruning method is introduced by following downward property, with respect of the maximum weight of a given database, to discard unimportant patterns. The proposed algorithm presents flexibility to user by providing intermediate unimportant pattern skipping opportunity and setting different starting positions in the time series sequence. The performance of our proposed algorithm is evaluated on real life datasets by varying different parameters. At the same time, a comparison between the proposed and an existing algorithm is shown, where the proposed approach outperformed the existing algorithm in terms of time and pattern generation.

Periodic pattern mining in weighted dynamic networks

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.