Corpus ID: 16929585

A Location-Based User Movement Prediction Approach For Geolife Project

  title={A Location-Based User Movement Prediction Approach For Geolife Project},
  author={hiyuan and Hen and Ahran and Anjabi and Ino and sa},
Recently obtaining knowledge from raw trajectory data has been an interest of many researches. Trajectory data set consists of thousands of records. To discover valuable knowledge from these records advanced data mining techniques must be applied. Models developed from these techniques will be useful for predication. In this paper data mining classification techniques are analyzed on trajectory dataset and Performance of these techniques is evaluated with recall, precision, kappa and accuracy… Expand

Figures and Tables from this paper

Sensor-Movement-Robust Angle Estimation for 3-DoF Lower Limb Joints Without Calibration.
This approach is the first experimental implementation of IMUbased 3-DoF angle estimation for lower-limb joints without calibration and the robustness against sensor movement are demonstrated through data from multiple sets of IMUs. Expand


Mining user similarity based on location history
A framework, referred to as hierarchical-graph-based similarity measurement (HGSM), is proposed for geographic information systems to consistently model each individual's location history and effectively measure the similarity among users and outperforms related similarity measures, such as the cosine similarity and Pearson similarity measures. Expand
Learning transportation mode from raw gps data for geographic applications on the web
An approach based on supervised learning is proposed to automatically infer transportation mode from raw GPS data to enable context-aware computing based on user's present transportation mode and design of an innovative user interface for Web users. Expand
Mining Personally Important Places from GPS Tracks
This work proposes a two-step approach that discretized continuous GPS data into places and learns important places from the place features and was validated using real user data and shown to have good accuracy when applied in predicting not only important and frequent places, but also important and not so frequent places. Expand
Mining interesting locations and travel sequences from GPS trajectories
This work first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG), and proposes a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location. Expand
Mobile Habits: Inferring and Predicting User Activities with a Location-Aware Smartphone
This work employs a fusion of wireless positioning methods available in current smartphones that offers high availability and accuracy without dedicated calibration, and demonstrates how such a positioning information can improve place extraction algorithms and enable the recognition of the new types of user activities both indoors and outdoors. Expand
WEKA Manual for Version 3-6-10
An Introduction to the WEKA Data Mining System Zdravko Markov Central an extended period of time (the first version of Weka was released 11 years In sum, the Weka team has made an outstandingExpand
Vii. References
A comparative analysis of actuator technologies for robotics " , At low frequencies, performance is quite good The small downward spike corresponds to the lowest impedance that could be generated onExpand
Analyzing Temporal Usage Patterns of Street Segments Based on GPS . s . l . , s . n . 4 ) Quannan Li 1 , 2 , Yu Zheng 2 , Xing Xie 2 , , 2008
  • Mining User Similarity Based on Location History . s . l . , 16 th ACM SIGSPATIAL international conference on Advances in geographic information systems . 5 )
  • 2012
Analyzing Temporal Usage Patterns of Street Segments Based on GPS. s.l., s.n
  • Analyzing Temporal Usage Patterns of Street Segments Based on GPS. s.l., s.n
  • 2012
WEKA Manual for version 3.6.7. s.l.:s.n
  • WEKA Manual for version 3.6.7. s.l.:s.n
  • 2012