Trajectory Patterns Mining and Activity Monitoring for Wild Life based on Temporal Tightness

February 2017
Vol-3, Issue-1
Paper ID: 3810
ISSN: 2395-4396
Downloads: 0

Abstract & Details

Research Area
Computer Engineering
Keywords
Trajectory Pattern mining Synchronous movement patterns Moving object trajectories Trajectory Clustering
Abstract
Trajectories are sequence that contain the temporal and Spatial information about movements.It is more useful in learning interactions between moving objects.There are multiple solutions defined in the previous methods those are inefficient and inconsistent due to it developed for specific type of trajectory patterns.Usually,user doesn't have an idea about which type of trajectories are hidden in their datasets therefore discovery of pattern get tedious task.Many trajectory patterns are arranged with respect to temporal and potential restrictions.Unifying patterns are nothing but mining trajectory patterns of various temporal tightness.It has two phases first one is to discover the detail level of patterns and another is to construct a forest that represents the various patterns.

Author Information

# Name Institute / Affiliation
1 Pallavi Rajendra Pathare Pravara Rural College of Engineering,Loni

How to Cite

Use the following formats to cite this article in your research.

APA Style
Pathare, Pallavi Rajendra (2017). Trajectory Patterns Mining and Activity Monitoring for Wild Life based on Temporal Tightness. International Journal of Advance Research and Innovative Ideas In Education, 3(1), 1203-1208.
MLA Style
Pathare, Pallavi Rajendra. "Trajectory Patterns Mining and Activity Monitoring for Wild Life based on Temporal Tightness." International Journal of Advance Research and Innovative Ideas In Education, vol. 3, no. 1, 2017, pp. 1203-1208.
IEEE Style
Pallavi Rajendra Pathare, "Trajectory Patterns Mining and Activity Monitoring for Wild Life based on Temporal Tightness," International Journal of Advance Research and Innovative Ideas In Education, vol. 3, no. 1, pp. 1203-1208, 2017.
Vancouver Style
Pathare Pallavi Rajendra. Trajectory Patterns Mining and Activity Monitoring for Wild Life based on Temporal Tightness. International Journal of Advance Research and Innovative Ideas In Education. 2017;3(1):1203-1208.
Harvard Style
Pathare, Pallavi Rajendra (2017) 'Trajectory Patterns Mining and Activity Monitoring for Wild Life based on Temporal Tightness', International Journal of Advance Research and Innovative Ideas In Education, 3(1), pp. 1203-1208.
Chicago Style
Pathare, Pallavi Rajendra. "Trajectory Patterns Mining and Activity Monitoring for Wild Life based on Temporal Tightness." International Journal of Advance Research and Innovative Ideas In Education 3, no. 1 (2017): 1203-1208.
Turabian Style
Pathare, Pallavi Rajendra. "Trajectory Patterns Mining and Activity Monitoring for Wild Life based on Temporal Tightness." International Journal of Advance Research and Innovative Ideas In Education 3, no. 1 (2017): 1203-1208.

Export Citation

Related Research

A Deep Learning-Based Framework for Mood-Oriented Music Recommendation Using Facial Expression Analysis
Vaibhav Ashok Bhangare et al. 2026 Computer Engineering
PDF Unavailable
Survey On : Intelligent Payroll and Human Resource Management Systems: A Systematic Review of Automation, Security, and Analytics
Vishakha Jadhav et al. 2026 Human Resource Management, Artificial Intelligence, Machine Learning, Payroll Systems, Cybersecurity, Business Intelligence, Robotic Process Automation, Employee Analytics, Digital Transforma
PDF Unavailable
Civic Engagement & Empowerment Platform
Supriya Dadaso Bankar et al. 2026 Computer engineering
PDF Unavailable
RAG System Development with Pydantic AI ChromaDB & Groq
Prof. Priyanka P. Kakade et al. 2026 Computer Engineering
PDF Unavailable
Machine Learning Based Early Stage Diabetes Detection System
Rohan Mulik et al. 2026 Computer Engineering
PDF Unavailable
A Survey on Skillsense:AI Career Analyzer App
Kirti Datir et al. 2026 Computer Engineering
PDF Unavailable
Employee Performance Portal
P.Harika et al. 2026 Computer science and engineering
PDF Unavailable