Space Breakdown Method A new approach for density-based clustering

Ardelean, Eugen-Richard; Stanciu, Alexander; Dînşoreanu, Mihaela; Potolea, Rodica; Lemnaru, Camelia; Moca, Vasile V.

doi:10.1109/iccp48234.2019.8959795

Cited by 3 publications

(10 citation statements)

References 9 publications

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“…Space Breakdown Method ( Ardelean et al, 2019 ) is a grid-based clustering algorithm that divides the N -dimensional feature space through the use of a grid with equidistant grid lines. Following a min-max normalisation given by Equation 1, the algorithm will convert the sample space into an N -dimensional array, where each N -dimensional hypercube has an equivalent in a cell of said array, this is done through the grid.…”

Section: Methodsmentioning

confidence: 99%

“…But the main steps of the algorithm have remained unchanged. The pseudocode for the improved SBM, modified to use a graph structure instead of the original array structure ( Ardelean et al, 2019 ), is shown next:…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, in certain cases of high dimensionality, a regular workstation may not be able to hold the amount of information needed for the auxiliary structure. An example was given in the discussion section of Ardelean et al (2019) : for a dataset with 1,000,000 points and 10 dimensions, even for a partition number as low as 5,5 10 chunks are created of which the majority are empty. Empty chunks do not need to be stored because no operations are performed on them.…”

Section: Methodsmentioning

confidence: 99%

“…The first modification in the algorithm intervenes in the chunkification step. Therefore, the first step of the algorithm, that of normalisation , remains unchanged from the original version ( Ardelean et al, 2019 ) and it consists of min-max normalisation of the input data X .…”

Section: Methodsmentioning

confidence: 99%

“…Space Breakdown Method (SBM) ( Ardelean et al, 2019 ) was developed as a deterministic clustering algorithm specialised for spike sorting. It was designed to deal with some of the major characteristics of such data: imbalanced clusters, overlapping clusters, and high data volume.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Improved space breakdown method – A robust clustering technique for spike sorting

Ardelean

Ichim²,

Dînşoreanu³

et al. 2023

Front. Comput. Neurosci.

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Space Breakdown Method (SBM) is a clustering algorithm that was developed specifically for low-dimensional neuronal spike sorting. Cluster overlap and imbalance are common characteristics of neuronal data that produce difficulties for clustering methods. SBM is able to identify overlapping clusters through its design of cluster centre identification and the expansion of these centres. SBM’s approach is to divide the distribution of values of each feature into chunks of equal size. In each of these chunks, the number of points is counted and based on this number the centres of clusters are found and expanded. SBM has been shown to be a contender for other well-known clustering algorithms especially for the particular case of two dimensions while being too computationally expensive for high-dimensional data. Here, we present two main improvements to the original algorithm in order to increase its ability to deal with high-dimensional data while preserving its performance: the initial array structure was substituted with a graph structure and the number of partitions has been made feature-dependent, denominating this improved version as the Improved Space Breakdown Method (ISBM). In addition, we propose a clustering validation metric that does not punish overclustering and such obtains more suitable evaluations of clustering for spike sorting. Extracellular data recorded from the brain is unlabelled, therefore we have chosen simulated neural data, to which we have the ground truth, to evaluate more accurately the performance. Evaluations conducted on synthetic data indicate that the proposed improvements reduce the space and time complexity of the original algorithm, while simultaneously leading to an increased performance on neural data when compared with other state-of-the-art algorithms.Code available athttps://github.com/ArdeleanRichard/Space-Breakdown-Method.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improved space breakdown method – A robust clustering technique for spike sorting

Ardelean

Ichim²,

Dînşoreanu³

et al. 2023

Front. Comput. Neurosci.

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Weighted Principal Component Analysis based on statistical properties of features for Spike Sorting

Aldea

Dînşoreanu

Potolea

et al. 2021

2021 IEEE 17th International Conference on Intelligent Computer Communication and Processing (ICCP)

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Sharp detection of oscillation packets in rich time-frequency representations of neural signals

Ardelean,

Bârzan,

Ichim

et al. 2023

Front. Hum. Neurosci.

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Brain oscillations most often occur in bursts, called oscillation packets, which span a finite extent in time and frequency. Recent studies have shown that these packets portray a much more dynamic picture of synchronization and transient communication between sites than previously thought. To understand their nature and statistical properties, techniques are needed to objectively detect oscillation packets and to quantify their temporal and frequency extent, as well as their magnitude. There are various methods to detect bursts of oscillations. The simplest ones divide the signal into band limited sub-components, quantifying the strength of the resulting components. These methods cannot by themselves cope with broadband transients that look like genuine oscillations when restricted to a narrow band. The most successful detection methods rely on time-frequency representations, which can readily show broadband transients and harmonics. However, the performance of such methods is conditioned by the ability of the representation to localize packets simultaneously in time and frequency, and by the capabilities of packet detection techniques, whose current state of the art is limited to extraction of bounding boxes. Here, we focus on the second problem, introducing two detection methods that use concepts derived from clustering and topographic prominence. These methods are able to delineate the packets’ precise contour in the time-frequency plane. We validate the new approaches using both synthetic and real data recorded in humans and animals and rely on a super-resolution time-frequency representation, namely the superlets, as input to the detection algorithms. In addition, we define robust tests for benchmarking and compare the new methods to previous techniques. Results indicate that the two methods we introduce shine in low signal-to-noise ratio conditions, where they only miss a fraction of packets undetected by previous methods. Finally, algorithms that delineate precisely the border of spectral features and their subcomponents offer far more valuable information than simple rectangular bounding boxes (time and frequency span) and can provide a solid foundation to investigate neural oscillations’ dynamics.

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Space Breakdown Method A new approach for density-based clustering

Cited by 3 publications

References 9 publications

Improved space breakdown method – A robust clustering technique for spike sorting

Improved space breakdown method – A robust clustering technique for spike sorting

Weighted Principal Component Analysis based on statistical properties of features for Spike Sorting

Sharp detection of oscillation packets in rich time-frequency representations of neural signals

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