Heterogeneous Data Clustering Research Articles

Optimizing data processing for edge-enabled IoT devices using deep learning based heterogeneous data clustering approach

- Edge-based Internet of Things devices have transformed smart farming, aiding in efficient data collection and processing for optimal resource utilization and crop yields. However, task scheduling and resource allocation pose significant challenges due to the dynamic nature of agricultural environments. Our research introduces a novel framework that integrates deep reinforcement learning algorithm into an edge-enabled wireless sensor network for multi-objective optimization of the functionality of the Deep Q-Networks (DQNs). This framework extends the traditional Q-learning method to manage large state-action spaces efficiently. It employs a deep neural network to approximate the Q-value function, rather than relying on a Q-table, making it more capable of handling complex problems with high-dimensional state spaces. It forms heterogenous data clusters supports an optimal task scheduling and resource allocation policies, sustains key objectives such as minimal energy consumption, latency, efficient resource utilization, and reduced task completion time. The framework's performance is evaluated in a simulated environment mimicking real-world smart farming applications. Results confirm its superiority in enhancing performance metrics and lowering energy consumption, as opposed to traditional networks.

Neural network analysis of the main challenges and threats to the economic security of the Russian Federation

Subject. The article addresses regional economy development from the standpoint of production, innovation, investment, and research activities in the regions of the Russian Federation, in the perspective of technological sovereignty and economic security of the country. Objectives. The aim is to solve a multifactorial problem describing the state of the economy in Russian regions by a new promising method, i.e. cluster analysis based on neural network modeling. Methods. To examine multidimensional statistical data, we employed the cluster analysis based on neural networks being an important branch of artificial intelligence. Results. We examined 85 regions and assessed them, using 12 indicators that we selected from the Rosstat website. The tools of artificial neural networks enabled the clustering of heterogeneous data. As a result, the Russian regions were located in six cluster formations. The paper assesses the influence of each indicator on cluster formation, demonstrates a significant inequality in the number of regions in clusters, highlights different levels of development of the regional economy, according to the totality of the considered indicators on cluster scale. Conclusions. The findings may help work out and implement strategies aimed at improving the balance of regional economy development, in the focus of technological sovereignty, under new threats and challenges to economic security.

Multidimensional heterogeneous data clustering algorithm for power transmission and transformation equipment

Considering that the operation of power transmission and transformation equipment is not timely discovered due to the untimely data integration, a multi-dimensional heterogeneous data clustering algorithm for power transmission and transformation equipment based on multimodal deep learning is proposed. The multi-modal deep learning method is used to mine relevant data and measure the similarity between the data, which can improve the accuracy of subsequent multi-bit heterogeneous data clustering of power transmission and transformation equipment. Set up a clustering center and process data clustering to complete multi-dimensional heterogeneous data clustering of power transmission and transformation equipment. The experimental results show that the method has high clustering accuracy in the clustering of voltage deviation overrun times, voltage harmonic total distortion rate overrun times, and voltage flicker overrun times.

Graph Laplacian for Heterogeneous Data Clustering in Sensor-Based Internet of Things

Traditional clustering algorithms are not suited for the heterogeneous data of the Sensor-Based Internet of Things. The accuracy of real-time data processing, in such applications, is further compromised because of the noise and missing values in the data. Considering the need for accurate clustering, a graph Laplacian-based heterogeneous data clustering is proposed in this work. Exploiting the correlation structure of the data, weight graphs are used to generate a graph Laplacian matrix to obtain co-related data points. Eigenvalues are further used to obtain distance-based, accurate clusters. The proposed algorithm is validated on five different real-world data sets and is able to outperform most of the existing algorithms. A detailed mathematical analysis followed by extensive simulation on real-world data sets proves the dexterity of the proposed method, as the performance gap, with respect to the state-of-the-art methods, in terms of accuracy and purity is as high as 30%.

Firefly Algorithm with Mini Batch K-Means Entropy Measure for Clustering Heterogeneous Categorical Timber Data

Clustering analysis is the process of identifying similar patterns in various types of data. Heterogeneous categorical data consists of data on ordinal, nominal, binary, and Likert scales. The clustering solution for heterogeneous data clustering remains difficult due to partitioning complex and dissimilarity features. It is necessary to find a solution to high-quality clustering techniques to efficiently determine the significant features of the data. This paper emphasizes using the firefly algorithm to reduce the distance gap between features and improve clustering performance. To obtain an optimal global solution for clustering, we proposed a hybrid of mini-batch k-means (MBK) clustering-based entropy distance measures (EM) with a firefly optimization algorithm (FA). This study compares the performance of hybrid K-Means, Agglomerative, DBSCAN, and Affinity clustering models with EM and FA. The evaluation uses a variety of data from the timber perception survey dataset. In terms of performance, the proposed MBK+EM+FA has superior and most effective clustering. It achieves a higher accuracy of 96.3 percent, a 97 percent F-measure, a 98 percent precision, and a 97 percent recall. Other external assessments revealed that the Homogeneity (HOMO) is 79.14 percent, the Fowlkes-Mallows Index (FMI) is 93.07 percent, the Completeness (COMP) is 78.04 percent, and the V-Measure (VM) is 78.58 percent. Both proposed MBK+EM+FA and MBK+EM took about 0.45s and 0.35s to compute, respectively. The excellent quality of the clustering results does not justify such time constraints. Surprisingly, the proposed model reduced the distance measure of all heterogeneous features. The future model could put heterogeneous categorical data from a different domain to the test.

Scalable incremental fuzzy consensus clustering algorithm for handling big data

Consensus clustering can produce novel, stable, and robust clustering results. Consensus clustering intends to merge a few existing basic segments into a coordinated one, and this has been broadly perceived as a promising solution for heterogeneous data clustering for big data. Even though many clustering algorithms have been proposed, getting a decent quality segment with high effectiveness is still not yet decided. In this paper, we propose a scalable incremental fuzzy consensus clustering (SIFCC) algorithm for a big data framework. It has been implemented on Apache Spark cluster framework, a distributed data stream environment for handling big data by considering the data as a set of data subsets that are processed incrementally. Sparks work great for iterative algorithms by supporting in-memory calculations, scalability, etc. SIFCC not only facilitates efficient big data clustering, but also improves the quality of clusters, performs storage space optimization, and time complexity during clustering. To establish the comparison, we designed and implemented the scalable model of existing fuzzy consensus clustering (FCC) on Apache Spark cluster, named as a scalable fuzzy consensus clustering (SFCC). Extensive experiments on real-world datasets show that the SIFCC algorithm achieves the better potential for clustering of Big Data in comparison with SFCC.

Initial Seed Selection for Mixed Data Using Modified K-means Clustering Algorithm

Data sets to which clustering is applied may be homogeneous (numerical or categorical) or heterogeneous (numerical and categorical) in nature. Handling homogeneous data is easier than heterogeneous data. We propose a novel technique for identifying initial seeds for heterogeneous data clustering, through the introduction of a unique distance measure where the distance of the numerical attributes is scaled such that it is comparable to that of categorical attributes. The proposed initial seed selection algorithm ensures selection of initial seed points from different clusters of the clustering solution which are then given as input to the modified K-means clustering algorithm along with the data set. This technique is independent of any user-defined parameter and thus can be easily applied to clusterable data sets with mixed attributes. We have also modified the K-means clustering algorithm to handle mixed attributes by incorporating our novel distance measure to handle numerical data and assigned the value one or zero when categorical data is dissimilar or similar. Finally, a comparison has been made with existing algorithms to bring out the significance of our approach. We also perform a statistical test to evaluate the statistical significance of our proposed technique.

Heterogeneous Data Clustering Considering Multiple User-provided Constraints

Clustering on heterogeneous networks which consist of multi-typed objects and links has proved to be a useful technique in many scenarios. Although numerous clustering methods have achieved remarkable success, current clustering methods for heterogeneous networks tend to consider only internal information of the dataset. In order to utilize background domain knowledge, we propose a general framework for clustering heterogeneous data considering multiple user-provided constrains. Specifically, we summarize that three types of manual constraints on the object can be used to guide the clustering process. Then we propose the User- HeteClus algorithm to solve the key issues in the case of star-structure heterogeneous data, which incorporating the user constraint into similarity measurement between central objects. Experiments on a real-world dataset show the effectiveness of the proposed algorithm.

Greedy optimization for K-means-based consensus clustering

Consensus clustering aims to fuse several existing basic partitions into an integrated one; this has been widely recognized as a promising tool for multi-source and heterogeneous data clustering. Owing to robust and high-quality performance over traditional clustering methods, consensus clustering attracts much attention, and much efforts have been devoted to develop this field. In the literature, the K-means-based Consensus Clustering (KCC) transforms the consensus clustering problem into a classical K-means clustering with theoretical supports and shows the advantages over the state-of-the-art methods. Although KCC inherits the merits from K-means, it suffers from the initialization sensitivity. Moreover, the current consensus clustering framework separates the basic partition generation and fusion into two disconnected parts. To solve the above two challenges, a novel clustering algorithm, named Greedy optimization of K-means-based Consensus Clustering (GKCC) is proposed. Inspired by the well-known greedy K-means that aims to solve the sensitivity of K-means initialization, GKCC seamlessly combines greedy K-means and KCC together, achieves the merits inherited by GKCC and overcomes the drawbacks of the precursors. Moreover, a 59-sampling strategy is conducted to provide high-quality basic partitions and accelerate the algorithmic speed. Extensive experiments on 36 benchmark datasets demonstrate the significant advantages of GKCC over KCC and KCC++ in terms of the objective function values and standard deviations and external cluster validity.

A High-Order Clustering Algorithm Based on Dropout Deep Learning for Heterogeneous Data in Cyber-Physical-Social Systems

An explosive growth of cyber-physical-social systems has been witnessed owing to the wide use of various mobile devices recently. A large volume of heterogeneous data has been collected from cyber-physical-social systems in the past few years. Each object in the heterogeneous dataset is typically multi-modal, posing a remarkable challenge on heterogeneous data clustering. In this paper, we propose a high-order k-means algorithm based on the dropout deep learning model for clustering heterogeneous objects in cyber-physical-social systems. We first build three dropout stacked auto-encoders, each with three hidden layers to learn the features for the different modalities of each object. Furthermore, we establish a feature tensor for each object by using the vector outer product to fuse the learned features. At last, we devise a tensor k-means algorithm to cluster the heterogeneous objects based on the tensor distance. We evaluate the proposed high-order k-means algorithm on two representative heterogeneous data sets and results imply that the proposed high-order k-means algorithm can achieve more accurate clustering results than other heterogeneous data clustering methods.

Distributed information-based clustering of heterogeneous sensor data

Heterogeneous sensing systems, consisting of sensors with different sensing capabilities, offer flexibility and provide multiple views of the sensed field by acquiring different types of measurements. The acquired sensor measurements are affected by different and unknown in number phenomena/sources of interest. To this end, a novel canonical correlation analysis (CCA) framework equipped with norm-one and norm-two regularization terms is designed to cluster the sensor data based on their information content. Block coordinate descent (BCD) is combined with the alternating direction method of multipliers (ADMM) framework to derive a centralized algorithm tackling the novel regularized CCA framework. Further, splitting of the regularized CCA into localized minimization subtasks across sensors enables distributed clustering of heterogeneous data based on their information content. Numerical tests demonstrate that the novel framework can achieve higher probability of correct clustering than existing alternatives.

Clustering Heterogeneous Data with k-Means by Mutual Information-Based Unsupervised Feature Transformation

Traditional centroid-based clustering algorithms for heterogeneous data with numerical and non-numerical features result in different levels of inaccurate clustering. This is because the Hamming distance used for dissimilarity measurement of non-numerical values does not provide optimal distances between different values, and problems arise from attempts to combine the Euclidean distance and Hamming distance. In this study, the mutual information (MI)-based unsupervised feature transformation (UFT), which can transform non-numerical features into numerical features without information loss, was utilized with the conventional k-means algorithm for heterogeneous data clustering. For the original non-numerical features, UFT can provide numerical values which preserve the structure of the original non-numerical features and have the property of continuous values at the same time. Experiments and analysis of real-world datasets showed that, the integrated UFT-k-means clustering algorithm outperformed others for heterogeneous data with both numerical and non-numerical features.

Algorithms for Clustering of a Heterogeneous Data on the Example of Solution of the Medical Task

The paper describes the original algorithm of a heterogeneous data clustering is based on complex application of a set of measures of distances and clustering methods and multi-stage clustering. In the algorithm we use ranging of attributes the object on their importance for group and a choice of an optimum attributes set, ensemble approach to get the final clustering solution. The algorithm is realized in MixDC (Mixed Data Clustering) software system. The technique and results of the solution of a real problem of a medical data clustering in software system are described.