Algorithms for clustering data
Algorithms for clustering data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A multi-agent-based model for a negotiation support system in electronic commerce
Enterprise Information Systems
Information Systems Frontiers
Density-based clustering of data streams at multiple resolutions
ACM Transactions on Knowledge Discovery from Data (TKDD)
Robustness of density-based clustering methods with various neighborhood relations
Fuzzy Sets and Systems
An SVM-based machine learning method for accurate internet traffic classification
Information Systems Frontiers
On cluster tree for nested and multi-density data clustering
Pattern Recognition
Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application
MSDBSCAN: multi-density scale-independent clustering algorithm based on DBSCAN
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Constructing a decision support system for management of employee turnover risk
Information Technology and Management
A novel ant-based clustering algorithm using the kernel method
Information Sciences: an International Journal
A unique property of single-link distance and its application in data clustering
Data & Knowledge Engineering
Improving user experience with case-based reasoning systems using text mining and Web 2.0
Expert Systems with Applications: An International Journal
Computers in Human Behavior
Relaxed constraints support vector machine
Expert Systems: The Journal of Knowledge Engineering
A novel self-adaptive clustering algorithm for dynamic data
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
A novel ant-based clustering algorithm using Renyi entropy
Applied Soft Computing
Enhancing minimum spanning tree-based clustering by removing density-based outliers
Digital Signal Processing
An automatic method to determine the number of clusters using decision-theoretic rough set
International Journal of Approximate Reasoning
Short communication: Algorithm to determine ε-distance parameter in density based clustering
Expert Systems with Applications: An International Journal
Fuzzy and crisp clustering methods based on the neighborhood concept: A comprehensive review
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - FUZZYSS'2011: 2nd International Fuzzy Systems Symposium
Hi-index | 0.00 |
Density-based clustering algorithms are attractive for the task of class identification in spatial database. However, in many cases, very different local-density clusters exist in different regions of data space, therefore, DBSCAN method [M. Ester, H.-P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in: E. Simoudis, J. Han, U.M. Fayyad (Eds.), Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, OR, AAAI, Menlo Park, CA, 1996, pp. 226-231] using a global density parameter is not suitable. Although OPTICS [M. Ankerst, M.M. Breunig, H.-P. Kriegel, J. Sander, OPTICS: ordering points to identify the clustering structure, in: A. Delis, C. Faloutsos, S. Ghandeharizadeh (Eds.), Proceedings of ACM SIGMOD International Conference on Management of Data Philadelphia, PA, ACM, New York, 1999, pp. 49-60] provides an augmented ordering of the database to represent its density-based clustering structure, it only generates the clusters with local-density exceeds certain thresholds but not the cluster of similar local-density; in addition, it does not produce clusters of a data set explicitly. Furthermore, the parameters required by almost all the major clustering algorithms are hard to determine although they significantly impact on the clustering result. In this paper, a new clustering algorithm LDBSCAN relying on a local-density-based notion of clusters is proposed. In this technique, the selection of appropriate parameters is not difficult; it also takes the advantage of the LOF [M.M. Breunig, H.-P. Kriegel, R.T. Ng, J. Sander, LOF: identifying density-based local outliers, in: W. Chen, J.F. Naughton, P.A. Bernstein (Eds.), Proceedings of ACM SIGMOD International Conference on Management of Data, Dalles, TX, ACM, New York, 2000, pp. 93-104] to detect the noises comparing with other density-based clustering algorithms. The proposed algorithm has potential applications in business intelligence.