The precise delineation of brain cancer is a crucial task during surgery. There are several techniques employed during surgical procedures to guide neurosurgeons in the tumor resection. However, hyperspectral imaging (HSI) is a promising non-invasive and non-ionizing imaging technique that could improve and complement the currently used methods. The HypErspectraL Imaging Cancer Detection (HELICoiD) European project has addressed the development of a methodology for tumor tissue detection and delineation exploiting HSI techniques. In this approach, the K-means algorithm emerged in the delimitation of tumor borders, which is of crucial importance. The main drawback is the computational complexity of this algorithm. This paper describes the development of the K-means clustering algorithm on different parallel architectures, in order to provide real-time processing during surgical procedures. This algorithm will generate an unsupervised segmentation map that, combined with a supervised classification map, will offer guidance to the neurosurgeon during the tumor resection task. We present parallel K-means clustering based on OpenMP, CUDA and OpenCL paradigms. These algorithms have been validated through an in-vivo hyperspectral human brain image database. Experimental results show that the CUDA version can achieve a speed-up of ~ 150 × with respect to a sequential processing. The remarkable result obtained in this paper makes possible the development of a real-time classification system.

Parallel K-Means Clustering for Brain Cancer Detection Using Hyperspectral Images

Clustering is an important data analysis technique, which has been applied to many practical scenarios. However, many partitioning based clustering algorithms are sensitive to the initial state of cluster centroids, may get trapped in a local optimum, and have poor robustness. In recent years, particle swarm optimization (PSO) has been regarded as an effective solution to the problem. However, it has the possibility of converging to a local optimum, especially when solving complex problems. In this paper, we propose a hybrid PSO-K-means algorithm, which uses the Gaussian estimation of distribution method (GEDM) to assist PSO in updating the population information and adopts Levy flight to escape from the local optimum. The proposed algorithm is named a GEDM and Levy flight based PSO-K-means (GLPSOK) clustering algorithm. Firstly, during initialization, a few particles are initialized using the cluster centroids generated by K-means, while other particles are randomly initialized in the search space. Secondly, GEDM and PSO are selected with different probability to update the population information at different optimization stages. Thirdly, Levy flight is adopted to help the search escape from the local optimum. Finally, the greedy strategy is carried out to select the promising particles from the parents and the newly generated candidates. Experimental results on both synthetic data sets and real-world data sets show that the proposed algorithm can produce better clustering results and is more robust than existing classic or state-of-the-art clustering algorithms.

/pdf/a-novel-hybrid-pso-k-means-clustering-algorithm-using-4jn1ocm9gl.pdf

A Novel Hybrid PSO-K-Means Clustering Algorithm Using Gaussian Estimation of Distribution Method and Lévy Flight

The advancement of data mining technology presents a way to examine and analyse the medical databases. Microarray data help in analysing the gene expressions, and the process of clustering helps in categorizing the data into organized groups. Grouping similar gene expressions paves the way for effective analysis, and the relationship between the expressions can be figured out. Recognizing the benefits of clustering, this work intends to present a clustering algorithm by combining generalized hierarchical fuzzy C means (GHFCM) and grey wolf optimization (GWO) algorithms. The GWO algorithm is utilized for selecting the initial clustering point, and the GHFCM algorithm is employed for clustering the microarray gene data. The performance of the proposed clustering algorithm is tested with respect to precision, recall, F-measure and time consumption, and the results are compared with the existing approaches. The performance of the proposed work is satisfactory with better F-measure rates and minimal time consumption.

A novel clustering algorithm by clubbing GHFCM and GWO for microarray gene data

The class-imbalance learning is one of the most significant research topics in the data mining and machine learning. Imbalance problem means that one of the classes has much more samples than that ...

Imbalanced Data Classification Algorithm Based on Clustering and SVM

Parallel discovery of inherent clusters using massively threaded architectures is the solution for handling computational challenges raised by fat datasets in cluster analysis. The Graphics Processing Unit and Compute Unified Device Architecture form a convincing platform to parallelize clustering algorithms. The parallel K-means algorithm aims at increasing the speedup, but often faces the hitch of falling into local minima. The heuristic search procedure to discover the global optima in the solution space is known as Tabu Search. The K-means clustering solution is fine-tuned by applying parallel implementation of Tabu Search K-means clustering in order to increase efficacy. The aim is to combine optimization characteristic of Tabu Search for calculation of centroids with clustering attitude of K-means and to enhance the solution using processing power of GPU. The parallelization strategy used exhibits increase in speedup. The parallel Tabu-KM algorithm is tested on standard datasets, and performance is ...

Optimal Tabu K-Means Clustering Using Massively Parallel Architecture

The study of medical datasets for analytical purpose is made possible by the innovation of different data mining techniques. Microarrays make simultaneous monitoring of genes under several conditions. Finding out co-expressed genes and coherent patterns is the main goal in bioinformatics research. Cluster analysis of gene expression data has been proven to be a valuable tool for finding biologically groups of genes. The mutual information criteria of the algorithm try to measure the dependency among gene variables. Simulated annealing is applied to solve the local minima problem of K-means algorithm. The improvements in the algorithm utilized further enhances with the use of parallelization techniques. The computational tasks in data mining can be effectively performed by graphics processing units (GPU). An optimized K-means implementation on the GPU using compute unified device architecture (CUDA) of NVIDIA is used as the programming environment. Importance is given on optimizations directly working on data parallel architecture to best use the computational capabilities available. The algorithm is performed in a hybrid manner, parallelizing simulated annealing K-means based on mutual information criteria (MIK). A performance study on medical dataset is performed, demonstrating a maximum 7$$\times $$ speed increase. Experimental analysis shows that the proposed method performs well on gene expression data. The performances of the new clustering methods are compared with those of some existing methods. It is seen that the clustering algorithm based on a combined metric of mutual information and Euclidean distance metric achieves the best performance.

Appliance of effective clustering technique for gene expression datasets using GPU

This paper deals with functioning and application of graphics processing units to general purpose computing and the high performance capability of a Graphics Processing Unit(GPU) using CUDA(Compute Unified Device Architecture ) to do parallel computing. GPGPU which stands for General-purpose computing on Graphics Processing Units is the technique in which the GPU is employed to handle and perform computations that were previously handled only by the CPU. Parallel computing is a form of computation in which many calculations are carried out simultaneously, operating on the principle that large problems can often be divided into smaller ones, which are then solved concurrently. There are many advantages in doing so, primary amongst which is speed, but unfortunately, getting the GPU to handle tasks traditionally performed by the CPU isn’t quite so simple .CUDA was developed by NVIDIA to execute simple programs using GPGPU which were executed on CPU. The logic behind the idea is that GPU consists of multi core processing units which operate in parallel and can be used to execute multiple instructions concurrently. CUDA gives program developers direct access to the virtual instruction set and memory of parallel computation elements in CUDA GPU’s.

https://www.rroij.com/open-access/gpu-acceleration-using-cuda-framework.pdf

GPU Acceleration Using CUDA Framework

A developed urban city is always accustomed to lots of cars on its roads but is alternatively not equipped with the required parking space. The increased scalability of the number of vehicles due to increase in population makes allotment of parking space challenging. The other difficulties faced are congestion created due to increased vehicles, wastage of space and time, traffic issues, difficulty in cruising etc. Every time the person driving a vehicle enters the parking lot, they attempt to find an empty space for the vehicle to be parked which leads to loss of time and energy, especially during peak hours. Smart Parking Assistant provides an optimized solution to overcome the above problems. It also provides a sophisticated parking assistance in economical cars. It mainly deals to avoid crash and confusion during car parking in unorganized and unknown parking areas.

IoT Enabled Smart Parking using Image Processing

Clustering on GPU – A Brief Survey

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