Apache Spark based kernelized fuzzy clustering framework for single nucleotide polymorphism sequence analysis

Jha, Preeti; Tiwari, Aruna; Bharill, Neha; Ratnaparkhe, Milind B.; Mounika, Mukkamalla; Nagendra, Neha

doi:10.1016/j.compbiolchem.2021.107454

Cited by 25 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fuzzy theory possesses significant advantage in dealing with uncertain problems [ 18 ]. FCM algorithm is an unsupervised image segmentation method widely applied in image segmentation [ 19 , 20 ], and it is characterized by high sensitivity as well as accuracy and wide application in medical field. Joloudari et al [ 21 ] applied FCM deep neural network (FCM-DNN) in cardiac magnetic resonance CAD imaging dataset and found out that the accuracy of the proposed FCM-DNN model reached 99.91%, which indicated that the model achieved the optimal performance.…”

Section: Discussionmentioning

confidence: 99%

Improved Fuzzy C-Means Clustering Algorithm-Based Dynamic Contrast-Enhanced Magnetic Resonance Imaging Features in the Diagnosis of Invasive Breast Carcinoma before and after Menopause

Wang

2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

The application effect of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on the improved fuzzy C-mean clustering (GA-PFCM) algorithm in analyzing premenopausal and postmenopausal invasive breast carcinoma was discussed. 159 patients with breast carcinoma were selected and divided into the postmenopausal group (71 patients) and the premenopausal group (88 patients) according to their menstrual status. The magnetic resonance images of the two groups were processed and analyzed using GA-PFCM algorithm, and the imaging characteristics and relevant parameters of DCE-MRI examination of the two groups were analyzed. Besides, the sensitivity, specificity, and accuracy of the diagnosis of invasive breast carcinoma by DCE-MRI examination were investigated. The results showed that the percentage of patients with lobulated lumps, patients with burrs on lesion edge, and patients with uniformly enhanced tumors in the premenopausal group was larger than that in the postmenopausal group ( P < 0.05 ). In the postmenopausal group, TCI of 33 patients was shown as platform curves, and that of 34 patients was shown as outflow curves. In the premenopausal group, TCI of 39 patients was shown as platform curves, and that of 41 patients was shown as outflow curves with a high proportion. The number of patients with peak height time ranging between 130 s and 260 s and of patients with early signal enhancement rate ranging between 100% and 200% was large. In contrast, the number of patients with ADC value higher than 1.2 × 10 − 3 was the least. In this research, there were 128 patients with positive invasive breast carcinoma and 31 with negative invasive breast carcinoma by pathological examination. Based on DCE-MRI examination, there were 121 patients with positive invasive breast carcinoma and 38 with negative invasive breast carcinoma. The sensitivity, specificity, and accuracy of invasive breast carcinoma by DCE-MRI were 91.41%, 87.1%, and 90.57%, respectively. In addition, the positive and negative predictive values reached 96.69% and 71.05%, respectively. In summary, GA-PFCM algorithm can be applied in the processing and segmentation of DCE-MRI images, and DCE-MRI could better diagnose invasive breast carcinoma with positive guiding value.

show abstract

Section: Discussionmentioning

confidence: 99%

Improved Fuzzy C-Means Clustering Algorithm-Based Dynamic Contrast-Enhanced Magnetic Resonance Imaging Features in the Diagnosis of Invasive Breast Carcinoma before and after Menopause

Wang

2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…Kernel-based distance is an effective method for extracting information from high-dimensional data by translating the element from smaller-dimensional to higher-dimensional spaces [10]. For every mathematical procedure that can be stated in relationships of dot products, the mapping delivers a connection from linearity to non-linearity.…”

Section: Kernel Distancementioning

confidence: 99%

Unsupervised Kernel-Induced Fuzzy Possibilistic C-Means Technique in Investigating Real-World Data

Devi¹

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

The goal of this study is to break down a large dataset into meaningful groupings. Due to the vast dimension and significant resemblance seen among data, exploring divided clusters in real-world datasets is the most difficult assignment. As a result, this work proposes a fuzzy set-based unsupervised effective clustering technique that includes possibilistic memberships, and fuzzy membership degrees into the membership, weighted Cauchy kernel-based similarity measure and center equations. The empirical findings demonstrate the feasibility of the proposed effective clustering technique.

show abstract

“…Not much work is done on fuzzy clustering of massive mixed data using the latest distributed platforms like Spark. Jha et al [18] proposed an Apache Spark-based fuzzy clustering algorithm that utilizes kernel Radial Basis Functions (RBF) to discover clusters in high-dimensional genomics data.…”

Section: Related Workmentioning

confidence: 99%

Distributed fuzzy clustering algorithm for mixed-mode data in Apache SPARK

Akram

Alamgir

2022

J Big Data

View full text Add to dashboard Cite

Fuzzy clustering is an invaluable data mining technique that allows each data point to belong to more than one cluster with some degree of membership. It is widely employed in exploratory data mining to discover overlapping communities in social networks, find structure in spectral data, and capture user interests in recommendation systems. Nowadays, the variety and volume of data are increasing at a tremendous rate. Data is power; the massive data, along with an effective technique, can unravel valuable information. The existing fuzzy clustering algorithms do not perform well on massive heterogeneous datasets. Processing an enormous amount of data is beyond the capacity of a single processor. The need of the hour is to develop fuzzy clustering techniques that can work on a distributed framework for Big Data processing and can handle heterogeneous data. In this research, we evaluate the performance of the recently proposed algorithm for the Fuzzy clustering of mixed-mode data FCMD-MD (D’Urso and Massari in Inf Sci 505:513–534, 2019) with different real-world datasets. We develop a distributed FCMD-MD, a fuzzy clustering algorithm for mixed-mode data in Apache SPARK. The experimental results show that the algorithm is scalable, performs well in a distributed environment, and clusters enormous heterogeneous data with high accuracy. We also compared the performance of distributed FCMD-MD and the distributed k-medoid algorithm.

show abstract

Apache Spark based kernelized fuzzy clustering framework for single nucleotide polymorphism sequence analysis

Cited by 25 publications

References 33 publications

Improved Fuzzy C-Means Clustering Algorithm-Based Dynamic Contrast-Enhanced Magnetic Resonance Imaging Features in the Diagnosis of Invasive Breast Carcinoma before and after Menopause

Improved Fuzzy C-Means Clustering Algorithm-Based Dynamic Contrast-Enhanced Magnetic Resonance Imaging Features in the Diagnosis of Invasive Breast Carcinoma before and after Menopause

Unsupervised Kernel-Induced Fuzzy Possibilistic C-Means Technique in Investigating Real-World Data

Distributed fuzzy clustering algorithm for mixed-mode data in Apache SPARK

Contact Info

Product

Resources

About