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Correlative Density-Based Clustering
Journal of Computational and Theoretical Nanoscience, 2016Mountains, which heap up by densities of a data set, intuitively reflect the structure of data points. These mountain clustering methods are useful for grouping data points. However, the previous mountain-based clustering suffers from the choice of parameters which are used to compute the density.
Jia-Lin Hua, Jian Yu, Miin-Shen Yang
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Variable density based clustering
2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016The class of density-based clustering algorithms excels in detecting clusters of arbitrary shape. DBSCAN, the most common representative, has been demonstrated to be useful in a lot of applications. Still the algorithm suffers from two drawbacks, namely a non-trivial parameter estimation for a given dataset and the limitation to data sets with constant
Alexander Dockhorn +2 more
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Geometric Algorithms for Density-Based Data Clustering
International Journal of Computational Geometry & Applications, 2002Data clustering is a fundamental problem arising in many practical applications. In this paper, we present new geometric approximation and exact algorithms for the density-based data clustering problem in d-dimensional space ℝd (for any constant integer d ≥ 2).
Chen, Danny Z., Smid, Michiel, Xu, Bin
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Aggregation Pheromone Density Based Clustering
9th International Conference on Information Technology (ICIT'06), 2006Ants, bees and other social insects deposit pheromone (a type of chemical) in order to communicate between the members of their community. Pheromone that causes clumping or clustering behavior in a species and brings individuals into a closer proximity is called aggregation pheromone. This paper presents a new algorithm (called APC) for clustering data
Megha Kothari +2 more
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Density-based clustering with topographic maps
IEEE Transactions on Neural Networks, 1999A new unsupervised competitive learning rule is introduced, called the kernel-based Maximum Entropy learning Rule (kMER), for equiprobabilistic topographic map formation. The application envisaged is density-based clustering. An empirical study is conducted to compare the clustering performance of kMER with that of a number of other unsupervised ...
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Density-Based Clustering with Differential Privacy
Information ScienceszbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fuyu Wu, Mingjing Du, Qiang Zhi
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Scalable density-based subspace clustering
Proceedings of the 20th ACM international conference on Information and knowledge management, 2011For knowledge discovery in high dimensional databases, subspace clustering detects clusters in arbitrary subspace projections. Scalability is a crucial issue, as the number of possible projections is exponential in the number of dimensions. We propose a scalable density-based subspace clustering method that steers mining to few selected subspace ...
Müller, Emmanuel +3 more
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Density based Projection Pursuit Clustering
2012 IEEE Congress on Evolutionary Computation, 2012Clustering of high dimensional data is a very important task in Data Mining. In dealing with such data, we typically need to use methods like Principal Component Analysis and Projection Pursuit, to find interesting lower dimensional directions to project the data and hence reduce their dimensionality in a manageable size. In this work, we propose a new
Sotiris K. Tasoulis +3 more
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DBDC: Density Based Distributed Clustering
2004Clustering has become an increasingly important task in modern application domains such as marketing and purchasing assistance, multimedia, molecular biology as well as many others. In most of these areas, the data are originally collected at different sites.
Eshref Januzaj +2 more
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