OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
A seller's perspective characterization methodology for online auctions
Proceedings of the 10th international conference on Electronic commerce
Clustering Uncertain Data Via K-Medoids
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Efficiently Clustering Probabilistic Data Streams
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Classification of tasks using machine learning
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Exploiting contexts to deal with uncertainty in classification
Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
Data selection for exact value acquisition to improve uncertain clustering
WAIM'10 Proceedings of the 11th international conference on Web-age information management
A discretization algorithm for uncertain data
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
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
Feature selection with mutual information for uncertain data
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
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
Scalable parallel OPTICS data clustering using graph algorithmic techniques
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
EMU: An expectation maximization based approach for clustering uncertain data
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
| Hi-index | 0.00 |
The hierarchical density-based clustering algorithm OPTICS has proven to help the user to get an overview over large data sets. When using OPTICS for analyzing uncertain data which naturally occur in many emerging application areas, e.g. location based services, or sensor databases, the similarity between uncertain objects has to be expressed by one numerical distance value. Based on such single-valued distance functions OPTICS, like other standard data mining algorithms, can work without any changes. In this paper, we propose to express the similarity between two fuzzy objects by distance probability functions which assign a probability value to each possible distance value. Contrary to the traditional approach, we do not extract aggregated values from the fuzzy distance functions but enhance OPTICS so that it can exploit the full information provided by these functions. The resulting algorithm FOPTICS helps the user to get an overview over a large set of fuzzy objects.