An Effective Density-Based Hierarchical Clustering Technique to Identify Coherent Patterns from Gene Expression Data

Sarmah, Sauravjyoti; Sarmah, Rosy; Bhattacharyya, Dhruba K.

doi:10.1007/978-3-642-20841-6_19

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…The essential concept behind density-based clustering is that for each object in a cluster, the neighborhood of a specified radius (eps) must contain at least a minimum number of objects (MinPts), that is, the neighborhood's cardinality must surpass a certain threshold. 34 According to the aforementioned definitions, the DBSCAN was created to effectively find groups and noise in the data. Two parameters are required by DBSCAN: (eps) and the lower number of points necessary to construct a cluster (Minpts).…”

Section: The Clustering-based Dbscan Algorithmmentioning

confidence: 99%

“…For example, clustering of complex objects, density-based clustering has ensured to be particularly successful for evaluating huge volume of heterogeneous and complicated data. 34 We implement the well-known and validated DBSCAN clustering algorithm, which is described in numerous references. DBSCAN does not need that the number of clusters is specified beforehand.…”

Section: The Clustering-based Dbscan Algorithmmentioning

confidence: 99%

“…Cluster is defined as the largest number of density‐connected points. The essential concept behind density‐based clustering is that for each object in a cluster, the neighborhood of a specified radius (eps) must contain at least a minimum number of objects (MinPts), that is, the neighborhood's cardinality must surpass a certain threshold 34 . According to the aforementioned definitions, the DBSCAN was created to effectively find groups and noise in the data.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…The DBSCAN algorithm is well‐suited to dealing with huge datasets, noise, and identifying clusters of various sizes and forms. For example, clustering of complex objects, density‐based clustering has ensured to be particularly successful for evaluating huge volume of heterogeneous and complicated data 34 . We implement the well‐known and validated DBSCAN clustering algorithm, which is described in numerous references.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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A new lossless electroencephalogram compression technique for fog computing‐based IoHT networks

Idrees

Khlief

2023

Int J Communication

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SummaryThe ability to design smarter, more predictive healthcare solutions for use in the community (at work and at home) and in healthcare facilities has been greatly enhanced by recent developments in the Internet of Health Things (IoHT) and cyber physical systems (CPS). These data collected by such medical sensors will be sent in a large quantity to the fog gateway of IoHT networks. These data should be forwarded to far cloud for further analysis and processing. Therefore, sending all of these data over the IoHT network to the data center will impose a significant burden on the IoHT network. This paper proposed a new lossless electroencephalogram (EEG) compression technique (NeLECoT) for fog computing‐based IoHT networks. It encodes the data of the patient at the fog gateway prior to sending it to the data center, thereby reducing the data's size. In the fog node, the NeLECoT combines three efficient techniques: clustering based on density‐based spatial clustering of applications with noise (DBSCAN), RLE (run length encoding), and Huffman encoding (HE). The clustering based on DBSCAN separates a massive volume of captured data into small groups of closely related (or similar) data. RLE encodes clustered EEG data, and the resulting file is encoded with HE. The fog gateway then transmits the encoded file to the cloud. Numerous simulation experiments were carried out, and the findings demonstrated that the suggested NeLECoT achieved better results than the competing techniques in terms of transmitted data size, compression ratio, compression power, compression time, decompression time, and average compression power.

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Section: The Clustering-based Dbscan Algorithmmentioning

confidence: 99%

Section: The Clustering-based Dbscan Algorithmmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

See 2 more Smart Citations

A new lossless electroencephalogram compression technique for fog computing‐based IoHT networks

Idrees

Khlief

2023

Int J Communication

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“…The GenClus algorithm presented in Sarmah et al (2010), is a density based clustering approach which finds useful subgroups of highly coherent genes within a cluster and obtains a hierarchical structure of the dataset where the sub-clusters give the finer clustering of the dataset. An effective tree-based clustering technique (GeneClusTree) for finding clusters in gene expression data is presented in Sarmah et al (2011). GeneClusTree finds all the clusters over subspaces using a tree-based density approach by scanning the whole database in minimum possible scans and is free from the restrictions of using a normal proximity measure.…”

Section: Introductionmentioning

confidence: 99%

An effective graph-based clustering technique to identify coherent patterns from gene expression data

Priyadarshini

Sarmah

Chakraborty

et al. 2012

IJBRA

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This paper presents an effective parameter-less graph based clustering technique (GCEPD). GCEPD produces highly coherent clusters in terms of various cluster validity measures. The technique finds highly coherent patterns containing genes with high biological relevance. Experiments with real life datasets establish that the method produces clusters that are significantly better than other similar algorithms in terms of various quality measures.

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TDAC: Co-Expressed Gene Pattern Finding Using Attribute Clustering

Rahman

Bhattacharyya

2014

Advances in Intelligent Systems and Computing

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An Effective Density-Based Hierarchical Clustering Technique to Identify Coherent Patterns from Gene Expression Data

Cited by 7 publications

References 20 publications

A new lossless electroencephalogram compression technique for fog computing‐based IoHT networks

A new lossless electroencephalogram compression technique for fog computing‐based IoHT networks

An effective graph-based clustering technique to identify coherent patterns from gene expression data

TDAC: Co-Expressed Gene Pattern Finding Using Attribute Clustering

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