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A Survey on Clustering Algorithms for Data Streams

Neha Sharma, Shraddha Masih, Pawan Makhija
2018 International Journal of Computer Applications  
The traditional clustering algorithms cannot be directly applied on the data streams.  ...  Clustering is one of the most useful technique for analsing stream data, as it does not require any predefined class labeling.  ...  Partition Based Methods An algorithm based on partition method tries to find out kpartitions based on some measurements. K-Means and K-Meadins based methods come into this category.  ... 
doi:10.5120/ijca2018918014 fatcat:pwiw7qjdgrh75lpzykizi3gbgy

A fuzzy incremental clustering approach to hybrid data discovery

Radu D. Găceanu, Horia F. Pop
2012 Acta Electrotechnica et Informatica  
We propose an incremental fuzzy clustering algorithm for hybrid data discovery. The algorithm is based on the ASM model where data items are represented by agents placed in a two dimensional grid.  ...  The algorithm allocates a new agent on the grid whenever a new data item arrives.  ...  In order to solve the clustering problem we propose an incremental algorithm based on ASM (Ants Sleeping Model) [2, 6] .  ... 
doi:10.2478/v10198-012-0010-x fatcat:3ysrxh35xvg5vm4hugoti23yam

Adaptive Neuro-Fuzzy Inference System Predictor with an Incremental Tree Structure Based on a Context-Based Fuzzy Clustering Approach

Chan-Uk Yeom, Keun-Chang Kwak
2020 Applied Sciences  
We propose an adaptive neuro-fuzzy inference system (ANFIS) with an incremental tree structure based on a context-based fuzzy C-means (CFCM) clustering process.  ...  The prediction experiment verified that the proposed CFCM-clustering-based ANFIS shows better prediction efficiency than the current grid-based and clustering-based ANFISs in the form of an incremental  ...  on grid-based ANFIS, FCM-clusteringbased ANFIS and the CFCM-clustering-based incremental tree structure.  ... 
doi:10.3390/app10238495 fatcat:gt7wlucstfebtosqilmtiy4tli

An incremental clustering method based on the boundary profile

Junpeng Bao, Wenqing Wang, Tianshe Yang, Guan Wu, Yong Deng
2018 PLoS ONE  
Obviously, for the clustering task, it is better to incrementally update the new clustering results based on the old data rather than to recluster all of the data from scratch.  ...  The incremental clustering approach is an essential way to solve the problem of clustering with growing Big Data.  ...  They presented an FP Tree based incremental clustering algorithm.  ... 
doi:10.1371/journal.pone.0196108 pmid:29677201 pmcid:PMC5909898 fatcat:i4qparkjibfsvbcqedsh6r2i34

A supervised clustering algorithm for computer intrusion detection

Xiangyang Li, Nong Ye
2005 Knowledge and Information Systems  
We previously developed a clustering and classification algorithm-supervised (CCAS) to learn patterns of normal and intrusive activities and to classify observed system activities.  ...  This robust CCAS adds data redistribution, a supervised hierarchical grouping of clusters and removal of outliers as the postprocessing steps.  ...  The core of CCAS is a grid-based incremental supervised clustering.  ... 
doi:10.1007/s10115-005-0195-8 fatcat:gssqtwazvzfz7bjs3l6nsdw6ce

SCUBA: Scalable Cluster-Based Algorithm for Evaluating Continuous Spatio-temporal Queries on Moving Objects [chapter]

Rimma V. Nehme, Elke A. Rundensteiner
2006 Lecture Notes in Computer Science  
In this paper, we propose, SCUBA, a Scalable Cluster Based Algorithm for evaluating a large set of continuous queries over spatiotemporal data streams.  ...  A moving cluster can serve as an approximation of the location of its members.  ...  For an elaborate survey on clustering, readers are referred to [19] . In our work, we adapt an incremental clustering algorithm, similar to the Leader-Follower clustering [16] .  ... 
doi:10.1007/11687238_58 fatcat:hgtfudg64jg7tnvewfgoudohjm

Mobile Photo Album Management with Multiscale Timeline

Kolbeinn Karlsson, Wei Jiang, Dong-Qing Zhang
2014 Proceedings of the ACM International Conference on Multimedia - MM '14  
clustering algorithm and its conventional incremental version.  ...  To address the slow speed of re-clustering when new photos are added, a new incremental spectral clustering algorithm is further developed, which is an order of magnitude faster than the traditional spectral  ...  To avoid frequent reclustering, we propose an incremental spectral clustering algorithm based on a well-known result from perturbation theory [10] .  ... 
doi:10.1145/2647868.2655060 dblp:conf/mm/KarlssonJZ14 fatcat:c2ym3sd2irdxtou4ppzhlc4mlu

Incremental Clustering Based on Swarm Intelligence [chapter]

Bo Liu, Jiuhui Pan, R I (Bob) McKay
2006 Lecture Notes in Computer Science  
grid.  ...  The algorithm applies information entropy to model behaviors of agents, such as picking up and dropping objects, and guides agent movement by pheromone in incremental stages.  ...  Our method is also based on ants' natural behaviors, but uses new measures to guide agents moving and picking up or dropping an item. 2 Incremental ant-based clustering In the dynamic system, our cluster  ... 
doi:10.1007/11903697_25 fatcat:z5e4hntds5ehpbmknmbfph4ude

Visualizing High-Dimensional Structure with the Incremental Grid Growing Neural Network [chapter]

Justine Blackmore, Risto Miikkulainen
1995 Machine Learning Proceedings 1995  
This paper proposes an algorithm that combines the topology-preserving characteristics of self-organizing maps with a exible, adaptive structure that learns the cluster boundaries in the data.  ...  Merge clustering extracts clusters, but it does not capture local or global topology.  ...  Because the algorithm is incremental, it may accomodate dynamic parameter tuning based on statistical properties of the intermediate structures.  ... 
doi:10.1016/b978-1-55860-377-6.50016-5 dblp:conf/icml/BlackmoreM95 fatcat:ytqqredkbfc73iym2jpklc5dde

A Survey on Clustering Algorithms for Partitioning Method

Hoda Khanali, Babak Vaziri
2016 International Journal of Computer Applications  
Clustering is one of the data mining methods. In all clustering algorithms, the goal is to minimize intracluster distances, and to maximize intercluster distances.  ...  Comparing various methods of the clustering, the contributions of the recent researches focused on solving the clustering challenges of the partition method.  ...  Moreover, hierarchical clustering is based on distance, density and continuity, density-based clustering is based on a density of data points, grid-based clustering is based on network structure, incremental  ... 
doi:10.5120/ijca2016912291 fatcat:apq7vblpmbdovknveptvbyabnm

Incremental Cluster Detection using a Soft Computing Approach

Alpa Reshamwala, Vijay Katkar, Mamta Ubnare
2010 International Journal of Computer Applications  
In this paper, we propose an incremental algorithm, IPYRAMID: Incremental Parallel hYbrid clusteRing using genetic progrAmming and Multiobjective fItness with Density employs a combination of data parallelism  ...  This issue is resolved in the proposed algorithm and density based incremental fitness function that helps to handle outliers.  ...  [6] Has proposed GDCA which is a Grid Density-based Clustering Algorithm and to make it useful in incremental environment it has proposed an Incremental Grid Density based Clustering Algorithm-IGDCA  ... 
doi:10.5120/1604-2155 fatcat:ylemfrurwjchdai3rjfojzdla4

New unsupervised clustering algorithm for large datasets

William Peter, John Chiochetti, Clare Giardina
2003 Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03  
Being unsupervised, the algorithm can also "rank" each cluster based on density.  ...  The method relies on weighting a dataset to grid points on a mesh, and using a small number of rule-based agents to find the high density clusters.  ...  Autoclass is an unsupervised algorithm based on Bayesian techniques for the automatic classification of data.  ... 
doi:10.1145/956750.956833 dblp:conf/kdd/PeterCG03 fatcat:6nx7xyrodfhibp47srnw3jbgwq

New unsupervised clustering algorithm for large datasets

William Peter, John Chiochetti, Clare Giardina
2003 Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03  
Being unsupervised, the algorithm can also "rank" each cluster based on density.  ...  The method relies on weighting a dataset to grid points on a mesh, and using a small number of rule-based agents to find the high density clusters.  ...  Autoclass is an unsupervised algorithm based on Bayesian techniques for the automatic classification of data.  ... 
doi:10.1145/956804.956833 fatcat:yuebsaubszdzxit7bxultbntdy

Using the fractal dimension to cluster datasets

Daniel Barbará, Ping Chen
2000 Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '00  
In this paper, we present a new clustering algorithm, based in the fractal properties of the data sets.  ...  FC requires one scan of the data, is suspendable at will, providing the best answer possible at that point, and is incremental.  ...  as Fractal Clustering for short, is a form of grid-based clustering where the space is divided in cells by a grid; other techniques that use grid-based clustering are STING 25 , WaveCluster 24 and Hierarchical  ... 
doi:10.1145/347090.347145 dblp:conf/kdd/BarbaraC00 fatcat:biqhohs2kvd65bkcmwkxgrfoca

Online and Scalable Unsupervised Network Anomaly Detection Method

Juliette Dromard, Gilles Roudiere, Philippe Owezarski
2017 IEEE Transactions on Network and Service Management  
Our solution relies on a discrete time-sliding window to update continuously the feature space and an incremental grid clustering to detect rapidly the anomalies.  ...  The experiments performed on the traffic of a core network of a Spanish intermediate Internet service provider demonstrated that ORUNADA detects in less than half a second an anomaly after its occurrence  ...  ORUNADA relies on a discrete time-sliding window and an incremental grid clustering algorithm allowing continuous network anomaly detection.  ... 
doi:10.1109/tnsm.2016.2627340 fatcat:zkt3ip2hprajlmhr6wc4u32xta
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