Creating Streaming Iterative Soft Clustering Algorithms

Hore, Prodip; Hall, Lawrence O.; Goldgof, Dmitry B.

doi:10.1109/nafips.2007.383888

Cited by 16 publications

(7 citation statements)

References 22 publications

(29 reference statements)

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“…Partitional clustering algorithms have been extended for single-pass and stream clustering. In [33] a general approach is proposed to enable traditional soft partitional clustering algorithms to deal with streaming data. The data stream is split into chunks and each chunk is partitioned into a set of cluster centroids.…”

Section: Related Workmentioning

confidence: 99%

Ant Colony Stream Clustering: A Fast Density Clustering Algorithm for Dynamic Data Streams

Fahy

Yang

Góngora

2019

IEEE Trans. Cybern.

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A data stream is a continuously arriving sequence of data and clustering data streams requires additional considerations to traditional clustering. A stream is potentially unbounded, data points arrive online and each data point can be examined only once. This imposes limitations on available memory and processing time. Furthermore, streams can be noisy and the number of clusters in the data and their statistical properties can change over time. This paper presents an online, bio-inspired approach to clustering dynamic data streams. The proposed ant colony stream clustering (ACSC) algorithm is a density-based clustering algorithm, whereby clusters are identified as high-density areas of the feature space separated by low-density areas. ACSC identifies clusters as groups of micro-clusters. The tumbling window model is used to read a stream and rough clusters are incrementally formed during a single pass of a window. A stochastic method is employed to find these rough clusters, this is shown to significantly speeding up the algorithm with only a minor cost to performance, as compared to a deterministic approach. The rough clusters are then refined using a method inspired by the observed sorting behavior of ants. Ants pick-up and drop items based on the similarity with the surrounding items. Artificial ants sort clusters by probabilistically picking and dropping micro-clusters based on local density and local similarity. Clusters are summarized using their constituent micro-clusters and these summary statistics are stored offline. Experimental results show that the clustering quality of ACSC is scalable, robust to noise and favorable to leading ant clustering and stream-clustering algorithms. It also requires fewer parameters and less computational time.

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Section: Related Workmentioning

confidence: 99%

Ant Colony Stream Clustering: A Fast Density Clustering Algorithm for Dynamic Data Streams

Fahy

Yang

Góngora

2019

IEEE Trans. Cybern.

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“…Most algorithms in this area [2,4,5,26] focus on two aspects: detecting outliers without taking concept drift tracking into consideration and clustering irregularly distributed data, which is a challenging direction of research in the field.…”

Section: Previous Work In the Fields Of Data Streammentioning

confidence: 99%

Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting

2017

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Data streams have arisen as a relevant topic during the last few years as an efficient method for extracting knowledge from big data. In the robust layered ensemble model (RLEM) proposed in this paper for shortterm traffic flow forecasting, incoming traffic flow data of all connected road links are organized in chunks corresponding to an optimal time lag. The RLEM model is composed of two layers. In the first layer, we cluster the chunks by using the Graded Possibilistic c-Means method. The second layer is made up by an ensemble of forecasters, each of them trained for short-term traffic flow forecasting on the chunks belonging to a specific cluster. In the operational phase, as a new chunk of traffic flow data presented as input to the RLEM, its memberships to all clusters are evaluated, and if it is not recognized as an outlier, the outputs of all forecasters are combined in an ensemble, obtaining in this a way a forecasting of traffic flow for a short-term time horizon. The proposed RLEM model is evaluated on a synthetic data set, on a traffic flow data simulator and on two real-world traffic flow data sets. The model gives an accurate forecasting of the traffic flow rates with outlier detection and shows a good adaptation to nonstationary traffic regimes. Given its characteristics of outlier detection, accuracy, and robustness, RLEM can be fruitfully integrated in traffic flow management systems.

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“…Recently, various other streaming algorithms [37–41] for an evolving distribution have been proposed. In [54, 55], we also introduced a streaming variant of the fuzzy-c-means algorithm for clustering evolving data streams. As stated earlier, our OFCM was designed to produce partition quality as good as clustering the full data set.…”

Section: Related Workmentioning

confidence: 99%

A Scalable Framework For Segmenting Magnetic Resonance Images

Hore

Hall

Goldgof

et al. 2008

J Sign Process Syst Sign Image Video Technol

Self Cite

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A fast, accurate and fully automatic method of segmenting magnetic resonance images of the human brain is introduced. The approach scales well allowing fast segmentations of fine resolution images. The approach is based on modifications of the soft clustering algorithm, fuzzy c-means, that enable it to scale to large data sets. Two types of modifications to create incremental versions of fuzzy c-means are discussed. They are much faster when compared to fuzzy c-means for medium to extremely large data sets because they work on successive subsets of the data. They are comparable in quality to application of fuzzy c-means to all of the data. The clustering algorithms coupled with inhomogeneity correction and smoothing are used to create a framework for automatically segmenting magnetic resonance images of the human brain. The framework is applied to a set of normal human brain volumes acquired from different magnetic resonance scanners using different head coils, acquisition parameters and field strengths. Results are compared to those from two widely used magnetic resonance image segmentation programs, Statistical Parametric Mapping and the FMRIB Software Library (FSL). The results are comparable to FSL while providing significant speed-up and better scalability to larger volumes of data.

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Creating Streaming Iterative Soft Clustering Algorithms

Cited by 16 publications

References 22 publications

Ant Colony Stream Clustering: A Fast Density Clustering Algorithm for Dynamic Data Streams

Ant Colony Stream Clustering: A Fast Density Clustering Algorithm for Dynamic Data Streams

Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting

A Scalable Framework For Segmenting Magnetic Resonance Images

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