Fat node leading tree for data stream clustering with density peaks

Detecting clusters of arbitrary shape and constantly delivering the results for newly arrived items are two critical challenges in the study of data stream clustering. However, the existing clustering methods could not deal with these two problems simultaneously. In this paper, we employ the density peaks based clustering (DPClust) algorithm to construct a leading tree (LT) and further transform it into a fat node leading tree (FNLT) in a granular computing way. FNLT is a novel interpretable synopsis of the current state of data stream for clustering. New incoming data is blended into the evolving FNLT structure quickly, and thus the clustering result of the incoming data can be delivered on the fly. During the interval between the delivery of the clustering results and the arrival of new data, the FNLT with blended data is granulated as a new FNLT with a constant number of fat nodes. The FNLT of the current data stream is maintained in a real-time fashion by the Blending-Granulating-Fading mechanism. At the same time, the change points are detected using the partial order relation between each pair of the cluster centers and the martingale theory. Compared to several state-of-the-art clustering methods, the presented model shows promising accuracy and efficiency.

[1]  Charu C. Aggarwal,et al.  Data Clustering , 2013 .

[2]  Bernhard Seeger,et al.  Cluster Kernels: Resource-Aware Kernel Density Estimators over Streaming Data , 2006, IEEE Transactions on Knowledge and Data Engineering.

[3]  João Gama,et al.  Hierarchical Clustering of Time-Series Data Streams , 2008, IEEE Transactions on Knowledge and Data Engineering.

[4]  Harry Wechsler,et al.  A Martingale Framework for Detecting Changes in Data Streams by Testing Exchangeability , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Ron Kohavi,et al.  Mining e-commerce data: the good, the bad, and the ugly , 2001, KDD '01.

[6]  Mohamed Medhat Gaber,et al.  Learning from Data Streams: Processing Techniques in Sensor Networks , 2007 .

[7]  Michèle Basseville,et al.  Detection of abrupt changes: theory and application , 1993 .

[8]  Philip S. Yu,et al.  A Framework for Projected Clustering of High Dimensional Data Streams , 2004, VLDB.

[9]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[10]  Edwin Lughofer,et al.  Autonomous data stream clustering implementing split-and-merge concepts - Towards a plug-and-play approach , 2015, Inf. Sci..

[11]  Vipin Kumar,et al.  Chameleon: Hierarchical Clustering Using Dynamic Modeling , 1999, Computer.

[12]  Ji Feng,et al.  A non-parameter outlier detection algorithm based on Natural Neighbor , 2016, Knowl. Based Syst..

[13]  Michèle Sebag,et al.  Data Stream Clustering With Affinity Propagation , 2014, IEEE Transactions on Knowledge and Data Engineering.

[14]  Li Tu,et al.  Stream data clustering based on grid density and attraction , 2009, TKDD.

[15]  James Bailey,et al.  Information theoretic measures for clusterings comparison: is a correction for chance necessary? , 2009, ICML '09.

[16]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[17]  Daniel A. Keim,et al.  A General Approach to Clustering in Large Databases with Noise , 2003, Knowledge and Information Systems.

[18]  Jing-Yu Yang,et al.  Density-based hierarchical clustering for streaming data , 2012, Pattern Recognit. Lett..

[19]  Alexandros Nanopoulos,et al.  Reverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection , 2015, IEEE Transactions on Knowledge and Data Engineering.

[20]  Guoyin Wang,et al.  DenPEHC: Density peak based efficient hierarchical clustering , 2016, Inf. Sci..

[21]  Philip S. Yu,et al.  Density-based clustering of data streams at multiple resolutions , 2009, TKDD.

[22]  Mingqiu Wang,et al.  Nonconvex penalized ridge estimations for partially linear additive models in ultrahigh dimension , 2015 .

[23]  Philip S. Yu,et al.  A Framework for Clustering Evolving Data Streams , 2003, VLDB.

[24]  Victoria J. Hodge,et al.  A Survey of Outlier Detection Methodologies , 2004, Artificial Intelligence Review.

[25]  Michael Hahsler,et al.  Clustering Data Streams Based on Shared Density between Micro-Clusters , 2016, IEEE Transactions on Knowledge and Data Engineering.

[26]  Alexander Hinneburg,et al.  DENCLUE 2.0: Fast Clustering Based on Kernel Density Estimation , 2007, IDA.

[27]  Sean Hughes,et al.  Clustering by Fast Search and Find of Density Peaks , 2016 .

[28]  Yifan Xu,et al.  Fast clustering using adaptive density peak detection , 2015, Statistical methods in medical research.

[29]  Aoying Zhou,et al.  Density-Based Clustering over an Evolving Data Stream with Noise , 2006, SDM.

[30]  Kensuke Koshijima,et al.  Change-Point Detection in a Sequence of Bags-of-Data , 2015, IEEE Trans. Knowl. Data Eng..

[31]  Christian Sohler,et al.  StreamKM++: A clustering algorithm for data streams , 2010, JEAL.

[32]  Edwin Lughofer,et al.  Extensions of vector quantization for incremental clustering , 2008, Pattern Recognit..

[33]  Tianrui Li,et al.  Hyper-ellipsoidal clustering technique for evolving data stream , 2014, Knowl. Based Syst..

[34]  Shonali Krishnaswamy,et al.  Mining data streams: a review , 2005, SGMD.

[35]  Sudipto Guha,et al.  Streaming-data algorithms for high-quality clustering , 2002, Proceedings 18th International Conference on Data Engineering.

[36]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[37]  Alessandro Laio,et al.  Clustering by fast search and find of density peaks , 2014, Science.