This research introduces the structure and elements of the system used to predict the user's interested location. The combination of DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm and GMM (Gaussian Mixture Model) algorithm is used to find locations where the user usually visits. In addition, the testing result of applying other clustering algorithms such as Gaussian Mixture model, Density Based clustering algorithm and K-means clustering algorithm on actual data are also shown as comparison. With having the knowledge of locations where the user usually visits, Discrete Bayesian Network is generated from the user's time-sequence location data. Combining the Bayesian Network, the user's current location and the time when the user left the other locations, the user's interested location can be predicted.

Creator

• Qiao, Junqing

Contributors

• Lai, Lifeng
• Huang, Xinming
• Padir, Taskin

Degree

• MS

Unit

• Electrical & Computer Engineering

Publisher

• Worcester Polytechnic Institute

Language

• English

Identifier

• etd-053016-140503

Keyword

• Navigation
• Bayesian Network
• Wheelchair
• Machine Learning

Advisor

• Padir, Taskin

Committee

• Lai, Lifeng
• Huang, Xinming

Defense date

• 2016-05-04

Year

• 2016

Date created

• 2016-05-30

Resource type

• Thesis

Rights statement

• In Copyright