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Clustering algorithms are part of algorithms of unsupervised-learning that are commonly used in different fields. Two major impacts on the data clustering algorithm have been the rapid developments and creation of electronic data. This includes the view of how the big data is stored as well as how it is processed. A cluster has a high level of resemblance to the same cluster and a low level of resemblance to other clusters. Clustering algorithms are commonly used in all fields, such as retail, banking, development, etc.. Even though different methodologies are proposed with different scenarios, but none of the existing methodologies are proven to be a better approaches. Here, in our work we have adopted an improved Map- Reduce programming model to combine the Canopy clustering and K-means clustering algorithms to process the Microarray data with the available commodity hardware with an aim to achieve better performance. The results obtained from different scenarios shown that the proposed method was capable of improving computational speed significantly by increasing the nodes as required. In this research, this paper explored the efficiency by evaluating and implementing the proposed Improved k-means with Improved Map Reduce algorithm which runs on Hadoop Frame work using Microarray dataset along with different datasets. [ABSTRACT FROM AUTHOR]

The churn prediction based on telecom data has been paid great attention because of the increasing the number telecom providers, but due to inconsistent data, sparsity, and hugeness, the churn prediction becomes complicated and challenging. Hence, an effective and optimal prediction of churns mechanism, named adaptive firefly-spider optimization (adaptive FSO) algorithm, is proposed in this research to predict the churns using the telecom data. The proposed churn prediction method uses telecom data, which is the trending domain of research in predicting the churns; hence, the classification accuracy is increased. However, the proposed adaptive FSO algorithm is designed by integrating the spider monkey optimization (SMO), firefly optimization algorithm (FA), and the adaptive concept. The input data is initially given to the master node of the spark framework. The feature selection is carried out using Kendall's correlation to select the appropriate features for further processing. Then, the selected unique features are given to the master node to perform churn prediction. Here, the churn prediction is made using a deep convolutional neural network (DCNN), which is trained by the proposed adaptive FSO algorithm. Moreover, the developed model obtained better performance using the metrics, like dice coefficient, accuracy, and Jaccard coefficient by varying the training data percentage and selected features. Thus, the proposed adaptive FSO-based DCNN showed improved results with a dice coefficient of 99.76%, accuracy of 98.65%, Jaccard coefficient of 99.52%. [ABSTRACT FROM AUTHOR]

Image fusion may be defined as an approach of merging two or more input images in to a single fused image which is comparatively more informative than all the input images. Image fusion aims to reduce the redundancy and uncertainty in the output and aims to maximize application related significant information. Fused images are used in diverse applications such as remote sensing, machine vision, biometrics, medical imaging and military domain. In this paper, we carry out iterative image fusion through fuzzy logic and neuro fuzzy logic techniques by fusing satellite images obtained from two or more image sensors to improve visualization capability. We also compare the proposed iterative image fusion approach based on fuzzy logic and neuro fuzzy logic with two more approaches like Principal component analysis (PCA) based fusion and the wavelet transform based image fusion. Fused images obtained from iterative image fusion using fuzzy and neuro fuzzy and other comparative methods are assessed through typical quality evaluation metrics. The proposed iterative fusion based on fuzzy logic approach divulges better values in some metrics whereas iterative image fusion based on neuro fuzzy logic technique provides better values in other metrics. Due to the potentiality of the fuzzy logic and neuro fuzzy logic. The overall experimental results generated from suggested approach substantiate that the utilization of the iterative image fusion using fuzzy logic and neuro fuzzy logic techniques would efficiently improve the quality grade of the fused image with concurrent confinement of spectral and spatial information. [ABSTRACT FROM AUTHOR]