Uncertain data mining: an example in clustering location data

Authors:
Michael Chau;Reynold Cheng;Ben Kao;Jackey Ng
Affiliations:
School of Business, The University of Hong Kong, Pokfulam, Hong Kong;Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong;Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong;School of Business, The University of Hong Kong, Pokfulam, Hong Kong
Venue:
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2006

Citing 4
Cited 14

Updating and Querying Databases that Track Mobile Units

Distributed and Parallel Databases - Special issue on mobile data management and applications
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Querying Imprecise Data in Moving Object Environments

IEEE Transactions on Knowledge and Data Engineering
Efficient indexing methods for probabilistic threshold queries over uncertain data

VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30

Approximation algorithms for clustering uncertain data

Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Clustering Uncertain Data Via K-Medoids

SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Improving Rules Quality Generated by Rough Set Theory for the Diagnosis of Students with LDs through Mixed Samples Clustering

RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Exploiting contexts to deal with uncertainty in classification

Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
Metric and trigonometric pruning for clustering of uncertain data in 2D geometric space

Information Systems
Kernel based K-medoids for clustering data with uncertainty

ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
UNN: a neural network for uncertain data classification

PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Uncertain centroid based partitional clustering of uncertain data

Proceedings of the VLDB Endowment
Distance-based feature selection on classification of uncertain objects

AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Attribute reduction of data with error ranges and test costs

Information Sciences: an International Journal
AN EFFICIENT REPRESENTATION MODEL OF DISTANCE DISTRIBUTION BETWEEN UNCERTAIN OBJECTS

Computational Intelligence
Improving classification accuracy on uncertain data by considering multiple subclasses

AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Review: Educational data mining: A survey and a data mining-based analysis of recent works

Expert Systems with Applications: An International Journal
EMU: An expectation maximization based approach for clustering uncertain data

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Data uncertainty is an inherent property in various applications due to reasons such as outdated sources or imprecise measurement. When data mining techniques are applied to these data, their uncertainty has to be considered to obtain high quality results. We present UK-means clustering, an algorithm that enhances the K-means algorithm to handle data uncertainty. We apply UK-means to the particular pattern of moving-object uncertainty. Experimental results show that by considering uncertainty, a clustering algorithm can produce more accurate results.