OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Density-based clustering for real-time stream data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Stream data clustering based on grid density and attraction
ACM Transactions on Knowledge Discovery from Data (TKDD)
GeoLife2.0: A Location-Based Social Networking Service
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Self-Adaptive Anytime Stream Clustering
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
C-DenStream: Using Domain Knowledge on a Data Stream
DS '09 Proceedings of the 12th International Conference on Discovery Science
Density-based data streams clustering over sliding windows
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
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Today, advances in hardware and storage techniques demand for automatically data mining on data streams. Clustering analysis is an importance tool on data streams mining. Though density-based clustering algorithms on data streams now could discover clusters of arbitrary shapes, their effectiveness are depended on parameters settings. Also global parameters used in these algorithms limit their ability in discovering overlapping clusters. In this paper, we propose a novel density-based clustering structure mining algorithm for data streams---OPCluStream. It could adaptively discover clusters of arbitrary shapes and overlapping clusters. Satisfying one-pass constraint, OPCluStream uses a tree topology to index points on which points link to other related ones using pointers directionally. This tree topology records relationships among points, which represent clustering results including a broad range of Eps settings and could discover clusters through a transformation to clustering structure. Clustering structure is equivalent to the index structure and convenient to be used. In addition, OPCluStream has a high efficiency on clustering since a usage of tree topology in points' index and a designed limited computing area when new points added to data streams. A number of experiments on synthetic and real data sets illustrate the effectiveness, efficiency and insights provided by our method.