Algorithms for clustering data
Algorithms for clustering data
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
On computing correlated aggregates over continual data streams
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Self-Organizing Maps
Cluster validity methods: part I
ACM SIGMOD Record
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Singularity and Slow Convergence of the EM algorithm for Gaussian Mixtures
Neural Processing Letters
Comparing Large Datasets Structures through Unsupervised Learning
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
A two-level clustering method using linear linkage encoding
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
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In recent years, the size and complexity of datasets have shown an exponential growth. In many application areas, huge amounts of data are generated, explicitly or implicitly containing spatial or spatiotemporal information. However, the ability to analyze these data remains inadequate, and the need for adapted data mining tools becomes a major challenge. In this paper, we propose a new unsupervised algorithm, suitable for the analysis of noisy spatiotemporal Radio Frequency IDentification (RFID) data. Two real applications show that this algorithm is an efficient data-mining tool for behavioral studies based on RFID technology. It allows discovering and comparing stable patterns in an RFID signal and is suitable for continuous learning.