Data Mining - Concepts and Techniques.

A privacy preserving association rule mining algorithm was introducedThis algorithm preserved privacy of individual values by computing scalar productMeanwhile the algorithm of computing scalar product was given and the security was analyzed

Privacy preserving association rule mining in vertically partitioned data

Due to the rapid growth of resource sharing, distributed systems are developed, which can be used to utilize the computations. Data mining (DM) provides powerful techniques for finding meaningful and useful information from a very large amount of data, and has a wide range of real‐world applications. However, traditional DM algorithms assume that the data is centrally collected, memory‐resident, and static. It is challenging to manage the large‐scale data and process them with very limited resources. For example, large amounts of data are quickly produced and stored at multiple locations. It becomes increasingly expensive to centralize them in a single place. Moreover, traditional DM algorithms generally have some problems and challenges, such as memory limits, low processing ability, and inadequate hard disk, and so on. To solve the above problems, DM on distributed computing environment [also called distributed data mining (DDM)] has been emerging as a valuable alternative in many applications. In this study, a survey of state‐of‐the‐art DDM techniques is provided, including distributed frequent itemset mining, distributed frequent sequence mining, distributed frequent graph mining, distributed clustering, and privacy preserving of distributed data mining. We finally summarize the opportunities of data mining tasks in distributed environment. WIREs Data Mining Knowl Discov 2017, 7:e1216. doi: 10.1002/widm.1216

Data mining in distributed environment: a survey

The feature ranking method is discussed based on Fisher discriminate analysis (FDA) and F-score.The relative distribution of different classes is considered in the paper.The method removes all insignificant features at a time, so it can effectively reduce computational cost.The advantages of the proposed method are discussed. F-score is a simple feature selection technique, however, it works only for two classes. This paper proposes a novel feature ranking method based on Fisher discriminate analysis (FDA) and F-score, denoted as FDAF-score, which considers the relative distribution of classes in a multi-dimensional feature space. The main idea is that a proper subset is got according to maximizing the proportion of average between-class distance to the relative within-class scatter. Because the method removes all insignificant features at a time, it can effectively reduce computational cost. Experiments on six benchmarking UCI datasets and two artificial datasets demonstrate that the proposed FDAF-score algorithm can not only obtain good results with fewer features than the original datasets as well as fast computation but also deal with the classification problem with noises well.

Feature selection based on FDA and F-score for multi-class classification

The industrial ecosystem has been unprecedentedly affected by the COVID-19 pandemic because of its immense contact restrictions. Therefore, the manufacturing and socio-economic operations that require human involvement have significantly intervened since the beginning of the outbreak. As experienced, the social-distancing lesson in the potential new-normal world seems to force stakeholders to encourage the deployment of contactless Industry 4.0 architecture. Thus, human-less or less-human operations to keep these IoT-enabled ecosystems running without interruptions have motivated us to design and demonstrate an intelligent automated framework. In this research, we have proposed "EdgeSDN-I4COVID" architecture for intelligent and efficient management during COVID-19 of the smart industry considering the IoT networks. Moreover, the article presents the SDN-enabled layer, such as data, control, and application, to effectively and automatically monitor the IoT data from a remote location. In addition, the proposed convergence between SDN and NFV provides an efficient control mechanism for managing the IoT sensor data. Besides, it offers robust data integration on the surface and the devices required for Industry 4.0 during the COVID-19 pandemic. Finally, the article justified the above contributions through particular performance evaluations upon appropriate simulation setup and environment.

/pdf/sdn-iot-empowered-intelligent-framework-for-industry-4-0-2s69b4j49q.pdf

SDN-IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic.

It selects sub-feature data from data miner database.It preserves the privacy of each individual party's data during collaborative computation.The model collects the correct number of sub-feature data from different data set.Fuzzy random variables help to select sub-feature data for generating special class. This paper addresses the selection of sub-feature from each feature using fuzzy methodologies maintaining the privacy during collection of data from participating parties in distributed environment. Based on fuzzy random variables conditional expectation is used in which two fuzzy sets are generated using Borel set that helps to determine sub-feature within certain interval. The privacy and selection of sub-feature leading to a distinguished class is the main objective of this research work. These two problems are directly related to data mining problems of classification and characterization of feature. In many cases traditional techniques are not suitable for complex databases. However our methodology provides better way for selection of sub-features under different situations. The proposed model and techniques both presents extensive theoretical analysis and experimental results. The experiments show the effectiveness and performance based on real world data set.

Privacy preserving sub-feature selection in distributed data mining

This article presents an analysis of data extraction for classification using correlation coefficient and fuzzy model. Several traditional methods of data extraction are used for classification that could not provide sufficient information for further step of data analysis on class. It needs refinement of features data to distinguish a class that differs from a traditional class. Thus, it proposes the feature tiny data (subfeature data) to find distinguish class from a traditional class using two methods such as correlation coefficient and fuzzy model to select features as well as subfeature for distinguishing class. In the first approach, the correlation coefficient methods with gradient descent technique are used to select features from the dataset and in the second approach, the fuzzy model with supreme of minimum value is considered to get subfeature data. As per the proposed model, some features (i.e., three features from the acoustic dataset, two features from the QCM dataset, and eight features from the audit dataset, etc.) and subfeatures (as per threshold value like 20 for acoustic; 10 for QCM, and 20 for audit, etc.) are selected based on correlation coefficient as well as fuzzy methods, respectively. Further, the probability approach is used to find the association and availability of subfeature data from the dimensional reduced database. The experimental results show the proposed framework identifies and selects both feature and subfeature data with the effectiveness of the new class. The comparison results of several classifiers on several datasets are explained in the experimental section.

Feature and Subfeature Selection for Classification Using Correlation Coefficient and Fuzzy Model

The novel coronavirus disease 2019 (COVID-19) has been a severe health issue affecting the respiratory system and spreads very fast from one human to other overall countries. For controlling such disease, limited diagnostics techniques are utilized to identify COVID-19 patients, which are not effective. The above complex circumstances need to detect suspected COVID-19 patients based on routine techniques like chest X-Rays or CT scan analysis immediately through computerized diagnosis systems such as mass detection, segmentation, and classification. In this paper, regional deep learning approaches are used to detect infected areas by the lungs' coronavirus. For mass segmentation of the infected region, a deep Convolutional Neural Network (CNN) is used to identify the specific infected area and classify it into COVID-19 or Non-COVID-19 patients with a full-resolution convolutional network (FrCN). The proposed model is experimented with based on detection, segmentation, and classification using a trained and tested COVID-19 patient dataset. The evaluation results are generated using a fourfold cross-validation test with several technical terms such as Sensitivity, Specificity, Jaccard (Jac.), Dice (F1-score), Matthews correlation coefficient (MCC), Overall accuracy, etc. The comparative performance of classification accuracy is evaluated on both with and without mass segmentation validated test dataset.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.12776

COVID-19 diagnosis system by deep learning approaches.

The feature selection addresses the issue of developing accurate models for classification in data mining. The aggregated data collection from distributed environment for feature selection makes the problem of accessing the relevant inputs of individual data records. Preserving the privacy of individual data is often critical issue in distributed data mining. In this paper, it proposes the privacy preservation of individual data for both feature and sub-feature selection based on data mining techniques and fuzzy probabilities. For privacy purpose, each party maintains their privacy as the instruction of data miner with the help of fuzzy probabilities as alias values. The techniques have developed for own database of data miner in distributed network with fuzzy system and also evaluation of sub-feature value included for the processing of data mining task. The feature selection has been explained by existing data mining techniques i.e., gain ratio using fuzzy optimization. The estimation of gain ratio based on the relevant inputs for the feature selection has been evaluated within the expected upper and lower bound of fuzzy data set. It mainly focuses on sub-feature selection with privacy algorithm using fuzzy random variables among different parties in distributed environment. The sub-feature selection is uniquely identified for better class prediction. The algorithm provides the idea of selecting sub-feature using fuzzy probabilities with fuzzy frequency data from data miner's database. The experimental result shows performance of our findings based on real world data set.

Privacy preserving sub-feature selection based on fuzzy probabilities

This article addresses the explainable machine learning for data extraction on diverse datasets. In many cases, individual or specific approaches have been developed for feature selection (FS) on a certain dataset, but collecting the diversity dataset and demonstrating it through different FS methods are challenging. Thus, this article proposed multiapproaches for FS with the classification of diverse datasets. The proposed framework is developed using various methods, such as extendable particle swarm optimization (PSO), global and local searching, feature ranking, feature clustering, computational cost-based FS, and multiobjective optimization. We effectively used these methods in our proposed work in a single-setting framework. We focused on three essential computational items in our framework: classification accuracy, selected features, and computational times. Due to the diverse dataset, few methods have been considered challenging during computational evaluation for classification accuracy with test cost. We tried to manage the classification accuracy based on total cost and high accuracy with less cost. The proposed framework is experimented with the above methods and analyzed through comparative results on diversity datasets. For example, when regular parameter values are in the range of 2⁻¹³-2⁻⁶, the evaluation result affects all items, i.e., decreasing during this range; other values do not affect results. We used thresholds ranging from 0.6 to 0.9 for highly correlated feature pairs as per the support vector machine (SVM) method for recursive feature elimination. 

Hemanta Kumar Bhuyan

Papers

Privacy preserving sub-feature selection in distributed data mining

Feature and Subfeature Selection for Classification Using Correlation Coefficient and Fuzzy Model

COVID-19 diagnosis system by deep learning approaches.

Privacy preserving sub-feature selection based on fuzzy probabilities

Explainable Machine Learning for Data Extraction Across Computational Social System