Behavior Pattern Mining Research Articles

STO2Vec: A Multiscale Spatio-Temporal Object Representation Method for Association Analysis

Spatio-temporal association analysis has attracted attention in various fields, such as urban computing and crime analysis. The proliferation of positioning technology and location-based services has facilitated the expansion of association analysis across spatio-temporal scales. However, existing methods inadequately consider the scale differences among spatio-temporal objects during analysis, leading to suboptimal precision in association analysis results. To remedy this issue, we propose a multiscale spatio-temporal object representation method, STO2Vec, for association analysis. This method comprises of two parts: graph construction and embedding. For graph construction, we introduce an adaptive hierarchical discretization method to distinguish the varying scales of local features. Then, we merge the embedding method for spatio-temporal objects with that for discrete units, establishing a heterogeneous graph. For embedding, to enhance embedding quality for homogeneous and heterogeneous data, we use biased sampling and unsupervised models to capture the association strengths between spatio-temporal objects. Empirical results using real-world open-source datasets show that STO2Vec outperforms other models, improving accuracy by 16.25% on average across diverse applications. Further case studies indicate STO2Vec effectively detects association relationships between spatio-temporal objects in a range of scenarios and is applicable to tasks such as moving object behavior pattern mining and trajectory semantic annotation.

A target behavior pattern mining and abnormal behavior monitoring based on multidimensional similarity metric

A target behavior pattern mining and abnormal behavior monitoring based on multidimensional similarity metric

Behavior pattern mining based on spatiotemporal trajectory multidimensional information fusion

Trajectory data mining is widely used in military and civil applications, such as early warning and surveillance system, intelligent traffic system and so on. Through trajectory similarity measurement and clustering, target behavior patterns can be found from massive spatiotemporal trajectory data. In order to mine frequent behaviors of targets from complex historical trajectory data, a behavior pattern mining algorithm based on spatiotemporal trajectory multidimensional information fusion is proposed in this paper. Firstly, spatial–temporal Hausdorff distance is proposed to measure multidimensional information differences of spatiotemporal trajectories, which can distinguish the behaviors with similar location but different course and velocity. On this basis, by combining the idea of k-nearest neighbor and density peak clustering, a new trajectory clustering algorithm is proposed to mine behavior patterns from trajectory data with uneven density distribution. Finally, we implement the proposed algorithm in simulated and radar measured trajectory data respectively. The experimental results show that the proposed algorithm can mine target behavior patterns from different complex application scenarios more quickly and accurately compared to the existing methods, which has a good application prospect in intelligent monitoring tasks.

Behavior Pattern Mining from Traffic and Its Application to Network Anomaly Detection

Accurately detecting and identifying abnormal behaviors on the Internet are a challenging task. In this work, an anomaly detection scheme is proposed that employs the behavior attribute matrix and adjacency matrix to characterize user behavior patterns. Then, anomaly detection is conducted by analyzing the residual matrix. By analyzing network traffic and anomaly characteristics, we construct the behavior attribute matrix, which incorporates seven features that characterize user behavior patterns. To include the effects of network environment, we employ the similarity between IP addresses to form the adjacency matrix. Further, we employ CUR matrix decomposition to mine the changing trends of the matrices and obtain the residual pattern characteristics that are used to detect anomalies. To validate the effectiveness and accuracy of the proposed scheme, two datasets are used: (1) the public MAWI dataset, collected from the WIDE backbone network, which is used to validate accuracy; (2) the campus network dataset, collected from the northwest center of Chinese Education and Research Network (CERNET), which is used to verify practicability. The experimental results demonstrate that the proposed scheme can not only accurately detect and identify abnormal behaviors but also trace the source of anomalies.

Analysis of moving cluster with scene constraints for group behavior pattern mining

Group behavior pattern mining in traffic scenarios is a challenging problem due to group variability and behavioral regionality. Most methods are either based on trajectory data stored in static databases regardless of the variability of group members or do not consider the influence of scene structures on behaviors. However, in traffic scenarios, information about group members may change over time, and objects' motions show regional characteristics owing to scene structures. To address these issues, we present a general framework of a moving cluster with scene constraints (MCSC) discovery consisting of semantic region segmentation, mapping, and an MCSC decision. In the first phase, a hidden Markov chain is adopted to model the evolution of behaviors along a video clip sequence, and a Markov topic model is proposed for semantic region analysis. During the mapping procedure, to generate snapshot clusters, moving objects are mapped into the corresponding sets of moving objects according to the semantic regions where they are located at each timestamp. In the MCSC decision phase, a candidate MCSC recognition algorithm and screening algorithm are designed to incrementally identify and output MCSCs. The effectiveness of the proposed approach is verified by experiments carried out using public road traffic data.

Retraction Note to: Normal discriminant deep convolution neural classification based web behavioral pattern mining for user identification

Retraction Note to: Normal discriminant deep convolution neural classification based web behavioral pattern mining for user identification

Automatic Mining Method of Group Behavior Patterns Based on Incremental Spatiotemporal Trajectory Big Data

There is a problem of unclear group clustering in group behavior pattern mining, which leads to a long mining time. An automatic group behavior pattern mining method based on incremental spatiotemporal trajectory big data is proposed. The grid sequence of each road segment and the road segment information included in each grid are obtained using the group behavior pattern trajectory network. Using incremental trajectory data, the properties of incremental spatiotemporal trajectory big data are retrieved, and the group behavior pattern is grouped. In the obtained class, all data element records are categorized according to their data elements. Multiple attribute dimensions, such as data definition, limitations, and feature words, are used to standardize the spatiotemporal trajectory data pieces. To complete the autonomous mining of group behavior patterns, all subsequences are visited, computed, and compared. The test results show that when the group size threshold is 20, the running time of the group behavior pattern automatic mining method based on incremental spatiotemporal trajectory big data is 311.66, which is 141.29 s and 148.66 s shorter than that based on DBSCAN and K-means, respectively. Therefore, this method has higher execution efficiency.

Exploring human behavior patterns and socio‐demographic factors based on American Time Use Survey

SummaryAs human activities in mobile environments are facing an ever‐increasing range of data, it is of great research importance to explore in depth the salient features of data that discriminate human behavior patterns and socio‐demographic factors. However, human activity behavior that consists of a series of complex spatio‐temporal activity processes is difficult to model. In this article, we develop a framework to perform activity behavior pattern mining and recognition, and the proposed framework has been applied to the American Time Use Survey to explore representative activity behavior patterns and socio‐demographic factors. The main contributions are as follows: (1) A method of activity behavior similarity is presented based on daily activities and activity sequences. (2) An activity sequence similarity algorithm with is proposed by line segment tree, greedy algorithm, and dynamic programming. (3) Representative activity behavior patterns and socio‐demographic factors are derived by clustering analysis and mining. (4) The activity behavior pattern is recognized by activity behavior features or socio‐demographic features. Through the experiments, we find that different daily activity behavior patterns are associated with specific socio‐demographic factors.

User behavior pattern mining and reuse across similar Android apps

Nowadays, Android apps have penetrated all aspects of our lives. Despite their popularity, understanding their behaviors is still a challenging task. Considering that many Android apps are in the same category and share similar workflows, in this paper, we propose a user behavior pattern mining and reuse approach across similar Android apps, thereby reducing the cost of understanding new apps. Particularly, for a specific new app, to figure out its typical behaviors, the behavior patterns that refer to the frequently-occurring workflows can be obtained from another similar app and transferred to this app. Moreover, to reuse the behavior patterns on this app, a semantic-based event fuzzy matching strategy and continuous workflow generation strategy are raised to generate workflows for this app. To evaluate our approach’s effectiveness and rationality, we conduct a series of experiments on 25 Android apps in five categories. Furthermore, the experimental results show that 88.3% of behavior patterns can be completely reused on similar apps, and the generated workflows cover 89.1% of the top 20% of important states.

Event Detection Through Differential Pattern Mining in Cyber-Physical Systems

Extracting knowledge from sensor data for various purposes has received a great deal of attention by the data mining community. For the purpose of event detection in cyber-physical systems (CPS), e.g., damage in building or aerospace vehicles from the continuous arriving data is challenging due to the detection quality. Traditional data mining schemes are used to reduce data that often use metrics, association rules, and binary values for frequent patterns as indicators for finding interesting knowledge about an event. However, these may not be directly applicable to the network due to certain constraints (communication, computation, bandwidth). We discover that, the indicators may not reveal meaningful information for event detection in practice. In this paper, we propose a comprehensive data mining framework for event detection in the CPS named DPminer, which functions in a distributed and parallel manner (data in a partitioned database processed by one or more sensor processors) and is able to extract a pattern of sensors that may have event information with a low communication cost. To achieve this, we introduce a new sensor behavioral pattern mining technique called differential sensor pattern (DSP) which considers different frequencies and values (non-binary) with a set of sensors, instead of traditional binary patterns. We present an algorithm for data preparation and then use a highly-compact data tree structure (called DP-Tree) for generating the DSP. An important tradeoff between the communication and computation costs for the event detection via data mining is made. Evaluation results show that DPminer can be very useful for networked sensing with a superior performance in terms of communication cost and event detection quality compared to existing data mining schemes.

Plagiarism Detection of Multi-threaded Programs Using Frequent Behavioral Pattern Mining

Software dynamic birthmark techniques construct birthmarks using the captured execution traces from running the programs, which serve as one of the most promising methods for obfuscation-resilient software plagiarism detection. However, due to the perturbation caused by non-deterministic thread scheduling in multi-threaded programs, such dynamic approaches optimized for sequential programs may suffer from the randomness in multi-threaded program plagiarism detection. In this paper, we propose a new dynamic thread-aware birthmark FPBirth to facilitate multi-threaded program plagiarism detection. We first explore dynamic monitoring to capture multiple execution traces with respect to system calls for each multi-threaded program under a specified input, and then leverage the Apriori algorithm to mine frequent patterns to formulate our dynamic birthmark, which can not only depict the program’s behavioral semantics, but also resist the changes and perturbations over execution traces caused by the thread scheduling in multi-threaded programs. Using FPBirth, we design a multi-threaded program plagiarism detection system. The experimental results based on a public software plagiarism sample set demonstrate that the developed system integrating our proposed birthmark FPBirth copes better with multi-threaded plagiarism detection than alternative approaches. Compared against the dynamic birthmark System Call Short Sequence Birthmark (SCSSB), FPBirth achieves 12.4%, 4.1% and 7.9% performance improvements with respect to union of resilience and credibility (URC), F-Measure and matthews correlation coefficient (MCC) metric, respectively.

An improved hidden behavioral pattern mining approach to enhance the performance of recommendation system in a big data environment

The proposed work aims to solve data sparsity problem in the recommendation system. It handles two-level pre-processing techniques to reduce the data size at the item level. Additional resources like items genre, tag, and time are added to learn and analyse the behaviour of the user preferences in-depth. The advantage of the proposed method is to recommend the item, based on user interest pattern and avoid recommending the outdated items. User information are grouped based on similar item genre and tag feature. This effectively handle overlapping conditions that exist on item’s genre, as it has more than one genre at initial level. Further, based on time, it analyses the user non-static interest. Overall it reduces the dimensions which is an initial way to prepare data, to analyse hidden pattern. To enhance the performance, the proposed method utilized Apache’s spark Mllib FP-Growth and association rule mining approach in a distributed environment. To reduce the computation cost of constructing tree in FP-Growth, the candidate data set is stored in matrix form. The experiments were conducted using MovieLens data set. The observed results shows that the proposed method achieves 4% increase in accuracy when compared to earlier methods.

Computational Experimental Study on Social Organization Behavior Prediction Problems

With the development of mobile Internet, behavioral trajectories of human life are more and more recorded, which makes it possible to use computer technology to mine organizational behavior patterns. The mining of organizational behavior patterns based on social computing can not only prepare them in a targeted manner but also predict the consequences of possible measures. The organization behavior pattern mining has achieved a series of achievements in the fields of e-commerce and enterprise management. However, the problem of class imbalance and nonconsistent misclassification cost is common in the field of organizational behavior. For this problem, this article compares and analyzes the performance of the organizational behavior prediction model established by four typical cost-sensitive learning methods based on six classifiers, which provides a basis for the appropriate selection of cost-sensitive learning methods in different situations. Among them, the upsampling learning method is a better cost-sensitive learning method. However, there are some shortcomings in the upper sampling method. In order to avoid the possible overfitting problem of the social organization behavior prediction model established by the upper sampling method, this article proposes a new cost-sensitive learning method suitable for the mining of organizational behavior patterns. Based on the cost curve, this article proposes an effective personalized solution to the problem of class disequilibrium and nonconsistent misclassification cost in organizational behavior prediction modeling.

RETRACTED ARTICLE: Normal discriminant deep convolution neural classification based web behavioral pattern mining for user identification

Mining the user behavior patterns from huge log files plays a major role in discovering the web user identity. Several data mining techniques describe the way of predicting the user identity using web access behavior patterns. Accurate prediction of the user identity is still a challenging issue. The proposed work introduces a normal discriminant Tanimoto similarity based deep convolution feedforward neural learning classification (NDTS-DCFNLC) technique to improve the user identifications with their web access behavior patterns. The NDTS-DCFNLC technique comprises multiple layers. Normal discriminant preprocessing is carried out in layer one to remove the unwanted patterns from the web access log files. Similarity among the relevant web pattern is calculated using Tanimoto similarity at layer two. Sigmoid activation function is used in output layer to classify the frequently accessed patterns with higher accuracy and minimum error based on similarity threshold. Experimental testing is done using apache weblog with different number of patterns and the diverse constraints such as classification accuracy, false positive rate, space requirements and execution time. Results were compared with linear temporal logic and KNN classification methods. The outcome shows NDTS-DCFNLC technique increases classification accuracy, decreases the false positives, execution time and the space requirements.

Visual analysis of customer switching behavior pattern mining for takeout service

Abstract Visualization of customer behavior is urgently needed for an increasing number of customer orders on takeout service. Although many works have been done on visualizing customer opinion or customer click events of one store, visualizing customer switching behavior among stores is still challenging. The challenge is to show customer order records over time and structure the inter-connection among different stores when customer switching behavior happens. In this work, we focus on takeout service to present a novel visual analysis system for retailers focusing on customer switching behavior patterns. Firstly we define five customer segments based on switching behavior. Then this system enables temporal-spatial driver exploration for different segments through several interactive views. Moreover, in order to visualize inter-connection sequences, augmented streamgraph with the bundled parallel coordinates is proposed as one alternative technique to visualize temporal event sequences. Evaluation including user and case studies also demonstrates the usefulness and effectiveness of this system in helping customer relationship management.

A Survey on Behavioral Pattern Mining From Sensor Data in Internet of Things

The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area.

Research Aligned Analysis on Web Access Behavioral Pattern Mining for User Identification

Human activity understanding includes activity recognition and activity pattern discovery. Monitoring human activity and finding abnormality in their activities used by many field like medical applications, security systems etc. Basically it helps and support in decision making systems. Mining user activity from web logs can helps in finding hidden information about the user access pattern which reveals the web access behaviour of the users. Clustering and Classification techniques are used for web user identification. Clustering is the task of grouping similar patterns for web user identification. Classification is the process of classifying web patterns for user identification. In this paper we have implemented the existing works and discussed the results here to find the limitations. In existing methods, many data mining techniques were introduced for web user behaviour identification. But, the user identification accuracy was not improved and time consumption was not reduced. Our objective is to study the existing work and explore the possibility to improve the identification accuracy and reduce the time consumption using machine learning and deep learning techniques.

Personalized information recommendation model based on context contribution and item correlation

The traditional information recommender system gives little consideration to the influence of contexts on users and correlations between items, thus affects the quality of personalized information recommendation service. In order to address the issue, the main contribution of this paper is to propose the measurement for context contribution and item correlation, and designs a novel personalized information recommendation model. First, the paper proposes an improved association rule algorithm based on FP-Tree to improve efficiency of user’s behavior pattern mining in big-data environment. Second, the paper puts forward a user interest extraction algorithm based on improved FP-Tree and context contribution to measure and model user preferences in personalized information recommendation service. Third, the paper presents an improved collaborative filtering algorithm based on item correlation. In order to deal with data sparseness, an “item-item” matrix is constructed by using frequent itemsets in association rules. Then, the paper uses context contribution to replace item score, which can improve the accuracy of the similarity measurement between items. Experiments show that the model is more effective and accurate than other existing methods.

Non-Negative Tensor Factorization for Human Behavioral Pattern Mining in Online Games

Multiplayer online battle arena is a genre of online games that has become extremely popular. Due to their success, these games also drew the attention of our research community, because they provide a wealth of information about human online interactions and behaviors. A crucial problem is the extraction of activity patterns that characterize this type of data, in an interpretable way. Here, we leverage the Non-negative Tensor Factorization to detect hidden correlated behaviors of playing in a well-known game: League of Legends. To this aim, we collect the entire gaming history of a group of about 1000 players, which accounts for roughly 100K matches. By applying our framework we are able to separate players into different groups. We show that each group exhibits similar features and playing strategies, as well as similar temporal trajectories, i.e., behavioral progressions over the course of their gaming history. We surprisingly discover that playing strategies are stable over time and we provide an explanation for this observation.

An empirical study of using sequential behavior pattern mining approach to predict learning styles

The learning style of a learner is an important parameter in his learning process. Therefore, learning styles should be considered in the design, development, and implementation of e-learning environments to increase learners’ performance. Thus, it is important to be able to automatically determine learning styles of learners in an e-learning environment. In this paper, we propose a sequential pattern mining approach to extract frequent sequential behavior patterns, which can separate learners with different learning styles. In this research, in order to recognize learners’ learning styles, system uses the Myers-Briggs Type Indicator’s (MBTI). The approach has been implemented and tested in an e-learning environment and the results show that learning styles of learners can be predicted with high accuracy. We show that learners with similar learning styles have similar sequential behavior patterns in interaction with an e-learning environment. A lot of frequent sequential behavior patterns were extracted which some of them have a meaningful relation with MBTI dimensions.