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A Survey on Clustering Algorithms for Data Streams
2018
International Journal of Computer Applications
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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
2012
Acta Electrotechnica et Informatica
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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
2020
Applied Sciences
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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
2018
PLoS ONE
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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
2005
Knowledge and Information Systems
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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]
2006
Lecture Notes in Computer Science
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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
2014
Proceedings of the ACM International Conference on Multimedia - MM '14
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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]
2006
Lecture Notes in Computer Science
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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]
1995
Machine Learning Proceedings 1995
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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
2016
International Journal of Computer Applications
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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
2010
International Journal of Computer Applications
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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
2003
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03
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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
2003
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03
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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
2000
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '00
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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
2017
IEEE Transactions on Network and Service Management
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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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