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Route mining for busy waterways is a challenging task. Complicated shipping routes may be generated due to vessels of different types congesting in a narrow water way, frequently changing navigational direction and weaving through multiple crossing traffic. The traditional way using visual bearing and ship-stationed techniques may mitigate hazards of ship collision but lack macroscopic information...
This paper presents a data mining technique for qualitative analysis of Hodgkin-Huxley model of cell excitability. Such problem cannot be solved analytically. Therefore we apply Monte-Carlo techniques for the generation of model parameters, and use data mining algorithm for classification of learning tuples obtained. As a result we attain a decision tree capable of classifying the excitability depending...
We propose a new methodology to detect social aspects of crowds in video sequences based on pedestrian features, which are obtained through image processing/computer vision techniques. The main idea is to apply and extend the concepts of Fundamental Diagram (FD) with more features, such as grouping and collectivity. Using crowd features we identify the crowd type and the main characteristics. In addition,...
Outlier Detection is one of the important research problems in temporal data mining. A pattern in time stamped temporal database is a sequence of probability values. Finding outliers from time stamped temporal databases requires suitable dissimilarity measure to find dissimilarity between input pattern and reference pattern which is of user interest. In the current work, the objective is to propose...
With the wide availability of GPS devices in our lives, massive amounts of object movement data have been collected from various moving object targets, such as mobile devices, animals, and vehicles. In the last decade, Moving Object Databases (MOD) have attracted many researchers. Analyzing such data has deep implications in many areas, such as ecological study and traffic control. In this study,...
We present a novel algorithm to identify reusable motion trajectories corresponding to the primitive actions in a human demonstration of a symbolic plan with accompanying narration. Our approach involves a multi-step process starting with time-series pattern mining applied to raw motion-capture data. We evaluated our algorithm on recordings of human motions and showed that it identifies reusable trajectories...
In the calculation process of spatial-temporal co-occurrence patterns, traditional methods often set the whole time frame as the actual existence time by default for all moving targets. However, in practice, existence time frame of different types is not necessarily the whole time frame. Based on this fact, the paper describes the calculation method of spatial-temporal interest degree-spatial frequency...
A technique for extracting the dominant spatiotemporal patterns of variability from high-dimensional simulation (measurement) trajectories is presented. The proposed framework integrates concepts from nonlinear dimensionality reduction methods with nonlinear Laplacian spectral analysis, to extract physically meaningful information about the temporal evolution of low-frequency electromechanical modes...
Mobility motif is a typical topology structure shared by movement trajectories, which could exhibit multi-scale spatio-temporal patterns in human behavior. There are two types of motifs, frequent, infrequent. The infrequent one hasn't got enough attention in research. In this paper, we study the relationship between infrequent motifs, mass activities. We have discovered that crowds are more likely...
Cluster analysis is a method of dividing the data into meaningful clusters based on the similarity of the data, making it possible to observe the data on a higher-level. Spatiotemporal data mining is a hot research topic in recent years. Currently, the research of spatiotemporal clustering analysis is still at a preliminary stage. Different from the existing methods for clustering spatiotemporal data,...
At present, it is a hot-spot in the research of data mining to mine mobility patterns on the basis of GPS trajectories. In order to solve the problems that data sampling frequency uncertainty, the value of experience as period parameter by human input, and spatiotemporal noises and outliers, we propose a framework called PMPM (Periodic Mobility Pattern Mining) to adaptively detect periodic parameters...
Traffic congestion is a major concern in many cities around the world. Previous work mainly focuses on the prediction of congestion and analysis of traffic flows, while the congestion correlation between road segments has not been studied yet. In this paper, we propose a three-phase framework to study the congestion correlation between road segments from multiple real world data. In the first phase,...
One of the most interesting areas of research is the analysis of road traffic. This includes vehicle path tracking, path prediction, intelligent vehicles, congestion detection and many more. Most of the research that has been done to detect traffic congestion used vehicular ad-hoc network (VANET) but of late data mining approach has been applied. Though most of the proposed work has successfully detected...
Tourist recommendation system are aimed at supporting the critical travel planning decisions the tourist face before starting the trip or on the course of travel. Users desire more efficient ways to find tourism recommendations which can save time and efforts. Geo-tagged photos on social media sites such as lickr provide plentiful location based data. Recently, there is an increased tendency to utilize...
Collaborative recommender system recommends items to the user based on what actions that other users in the same environment did in the past. This paper focuses on recommending popular places based on behavior of Global Positioning System (GPS) traces to multiple users. Collecting different reviews of popular places from multiple visitors is an explicit way to help Recommender System to rate these...
This paper proposes an audio watermarking scheme based on singular-spectrum analysis (SSA) and differential evolution. In our framework, a watermark is embedded into an audio signal by modifying the amplitude of some oscillatory components which are decomposed by SSA, and a parameter set for the modification is determined by differential evolution. Test results showed that, although there is a trade-off...
In this paper, a hierarchical and informative summarization framework is proposed, which facilitates rapid video browsing. Moreover, a method for loitering detection is exploited to indicate potential abnormal behaviors. The hierarchical framework includes two levels: a holistic-level and an object-level. The holistic-level summarization provides viewers with a comprehensive and compact representation...
As well-developed social networking services collect rich location data of mobile users, it becomes interesting for measuring the mobility relationship strength. The state-of-the-art measure for such relationship is Personal, Global and Temporal factors (PGT) method, which calculates personal and global background in the spatial domain. We argue that meeting events differentiate from each other not...
Photoplethysmography (PPG) is a kind of physiological information that can be easily disturbed by motion artifacts. Therefore,this paper paid attention to the extraction of information from PPG with motion artifacts. In the paper, a new framework that can extract heart rate(HR) information from PPG signals with severe motion artifacts is proposed, using Singular Spectrum Analysis, Real-time Clustering,...
College students with financial difficulties refer to those whose families can hardly afford their high tuition in universities, and should be supported by modern funding system. Indeed, students' economic plight negatively impact their mental health, academic performance, as well as their personal and social life. While funding students in financial hardship is widely accepted, there is limited understanding...
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