Algorithms for clustering data
Algorithms for clustering data
Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Clustering data with measurement errors
Computational Statistics & Data Analysis
Clustering of time series data-a survey
Pattern Recognition
Missing data imputation: a fuzzy K-means clustering algorithm over sliding window
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Tracklet descriptors for action modeling and video analysis
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Patterns of temporal variation in online media
Proceedings of the fourth ACM international conference on Web search and data mining
A novel clustering method on time series data
Expert Systems with Applications: An International Journal
Action selection via learning behavior patterns in multi-robot domains
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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Clustering is a very well studied problem that attempts to group similar data points. Most traditional clustering algorithms assume that the data is provided without measurement error. Often, however, real world data sets have such errors and one can obtain estimates of these errors. We present a clustering method that incorporates information contained in these error estimates. We present a new distance function that is based on the distribution of errors in data. Using a Gaussian model for errors, the distance function follows a Chi-Square distribution and is easy to compute. This distance function is used in hierarchical clustering to discover meaningful clusters. The distance function is scale-invariant so that clustering results are independent of units of measuring data. In the special case when the error distribution is the same for each attribute of data points, the rank order of pair-wise distances is the same for our distance function and the Euclidean distance function. The clustering method is applied to the seasonality estimation problem and experimental results are presented for the retail industry data as well as for simulated data, where it outperforms classical clustering methods.