BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Accelerating exact k-means algorithms with geometric reasoning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Extended K-means with an Efficient Estimation of the Number of Clusters
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Clustering objects on a spatial network
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Shapes based trajectory queries for moving objects
Proceedings of the 13th annual ACM international workshop on Geographic information systems
A fast k-means implementation using coresets
Proceedings of the twenty-second annual symposium on Computational geometry
Time-focused clustering of trajectories of moving objects
Journal of Intelligent Information Systems
The string edit distance matching problem with moves
ACM Transactions on Algorithms (TALG)
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Mining images using clustering and data compressing techniques
International Journal of Information and Communication Technology
A Dynamic Clustering Algorithm for Mobile Objects
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Continuous Clustering of Moving Objects in Spatial Networks
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Continuous Clustering of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Continuous k-Means Monitoring over Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Detecting Commuting Patterns by Clustering Subtrajectories
ISAAC '08 Proceedings of the 19th International Symposium on Algorithms and Computation
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Least squares quantization in PCM
IEEE Transactions on Information Theory
Hi-index | 0.00 |
k-means clustering algorithm is a famous clustering algorithm applied in many applications. However, traditional k-means algorithm assumes that the initial number of centroids is known in advance. This dependence on the number of clusters and the initial choice of the centroids affect both the performance and accuracy of the algorithm. To overcome this problem, in this paper, we propose a heuristic that dynamically calculates k based on the movement patterns in the trajectory dataset and optimally initialises the k centroids. We basically consider distinct similar moving patterns as an initialisation for the number of clusters (k). In addition, we design a scalable tool for mining moving object data through (an architecture composed of) a rich set of cluster refinement modules that operate on top of the moving object database enabling users to analyse trajectory data from different perspectives. We validate our approaches experimentally on both real and synthetic data and test the performance and accuracy of our techniques.