DENCLUE

DENCLUE 2.0: Fast Clustering Based on Kernel Density Estimation. The Denclue algorithm employs a cluster model based on kernel density estimation. A cluster is defined by a local maximum of the estimated density function. Data points are assigned to clusters by hill climbing, i.e. points going to the same local maximum are put into the same cluster. A disadvantage of Denclue 1.0 is, that the used hill climbing may make unnecessary small steps in the beginning and never converges exactly to the maximum, it just comes close. We introduce a new hill climbing procedure for Gaussian kernels, which adjusts the step size automatically at no extra costs. We prove that the procedure converges exactly towards a local maximum by reducing it to a special case of the expectation maximization algorithm. We show experimentally that the new procedure needs much less iterations and can be accelerated by sampling based methods with sacrificing only a small amount of accuracy.


References in zbMATH (referenced in 12 articles , 1 standard article )

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  1. Pfander, David; Daiß, Gregor; Pflüger, Dirk: Heterogeneous distributed big data clustering on sparse grids (2019)
  2. Chen, Bo; Ting, Kai Ming; Washio, Takashi; Zhu, Ye: Local contrast as an effective means to robust clustering against varying densities (2018)
  3. Matioli, L. C.; Santos, S. R.; Kleina, M.; Leite, E. A.: A new algorithm for clustering based on kernel density estimation (2018)
  4. Schneider, Johannes; Vlachos, Michail: Scalable density-based clustering with quality guarantees using random projections (2017)
  5. Sreevani; Murthy, C. A.: On bandwidth selection using minimal spanning tree for kernel density estimation (2016)
  6. Zhang, Ke; Xiong, Yingzhi; Huang, Lei; Chai, Yi: A novel algorithm based on avoid determining noise threshold in DENCLUE (2016)
  7. Khalid, Shehzad; Razzaq, Shahid: TOBAE: a density-based agglomerative clustering algorithm (2015)
  8. Mirzaie, Mansooreh; Barani, Ahmad; Nematbakkhsh, Naser; Mohammad-Beigi, Majid: Bayesian-OverDBC: a Bayesian density-based approach for modeling overlapping clusters (2015)
  9. Kawale, Jaya; Liess, Stefan; Kumar, Arjun; Steinbach, Michael; Snyder, Peter; Kumar, Vipin; Ganguly, Auroop R.; Samatova, Nagiza F.; Semazzi, Fredrick: A graph-based approach to find teleconnections in climate data (2013)
  10. Moshtaghi, Masud; Rajasegarar, Sutharshan; Leckie, Christopher; Karunasekera, Shanika: An efficient hyperellipsoidal clustering algorithm for resource-constrained environments (2011) ioport
  11. Chaoji, Vineet; Hasan, Mohammad Al; Salem, Saeed; Zaki, Mohammed J.: SPARCL: an effective and efficient algorithm for mining arbitrary shape-based clusters (2009) ioport
  12. Hinneburg, Alexander; Gabriel, Hans-Henning: DENCLUE 2.0: Fast clustering based on kernel density estimation (2007) ioport