Multi Clustering Research Articles

EEDC: An Energy Efficient Data Communication Scheme Based on New Routing Approach in Wireless Sensor Networks for Future IoT Applications.

Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have emerged as transforming technologies, bringing the potential to revolutionize a wide range of industries such as environmental monitoring, agriculture, manufacturing, smart health, home automation, wildlife monitoring, and surveillance. Population expansion, changes in the climate, and resource constraints all offer problems to modern IoT applications. To solve these issues, the integration of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has come forth as a game-changing solution. For example, in agricultural environment, IoT-based WSN has been utilized to monitor yield conditions and automate agriculture precision through different sensors. These sensors are used in agriculture environments to boost productivity through intelligent agricultural decisions and to collect data on crop health, soil moisture, temperature monitoring, and irrigation. However, sensors have finite and non-rechargeable batteries, and memory capabilities, which might have a negative impact on network performance. When a network is distributed over a vast area, the performance of WSN-assisted IoT suffers. As a result, building a stable and energy-efficient routing infrastructure is quite challenging in order to extend network lifetime. To address energy-related issues in scalable WSN-IoT environments for future IoT applications, this research proposes EEDC: An Energy Efficient Data Communication scheme by utilizing "Region based Hierarchical Clustering for Efficient Routing (RHCER)"-a multi-tier clustering framework for energy-aware routing decisions. The sensors deployed for IoT application data collection acquire important data and select cluster heads based on a multi-criteria decision function. Further, to ensure efficient long-distance communication along with even load distribution across all network nodes, a subdivision technique was employed in each tier of the proposed framework. The proposed routing protocol aims to provide network load balancing and convert communicating over long distances into shortened multi-hop distance communications, hence enhancing network lifetime.The performance of EEDC is compared to that of some existing energy-efficient protocols for various parameters. The simulation results show that the suggested methodology reduces energy usage by almost 31% in sensor nodes and provides almost 38% improved packet drop ratio.

Impact of the Validity Analysis Model and Multirelational Data Clustering Based on the Trust Probability

In view of the present poor impact of multi relational data clustering, a multirelational data clustering effectiveness analysis model based on trusted probability is created. The construction of the multirelational data clustering trusted probability evaluation model, standardization of the multirelational data clustering trusted probability evaluation index, optimization of the multirelational data clustering trusted probability analysis process, and data clustering processing quality and finally, investigations show that the validity analysis model of multirelational data clustering based on credible probability is more effective in practice and satisfies the research goals entirely.

Orthogonal Constrained Meta Heuristic Adaptive Multi-View Clustering over Multi Labeled Categorical Data Analysis

In data mining, clustering is the one of the efficient research concept in real time data analysis, evaluation of attribute representation in clustering is main issue in artificial intelligence related research areas. Multi labeled clustering gives high amount of valuable data, which describes the evaluation and representation of attribute be the trending concept in multi labeled categorical data analysis. Multi dimensional clustering is combined complementary data from different dimensions to provide efficient clustering results in various conditions. Different multi view clustering techniques are proposed traditionally but they can give output as single clustering with input data. Because of multiplicity, multi dimensional data can have different grouping data which are reasonable consist perspective attributes. So how to find measurable and reasonable cluster results which are represented in multi view labeled data is still challenging task, so that in this paper, we propose a novel approach i.e. Orthogonal Constrained Meta Heuristic Adaptive Multi-View Clustering (OCMHAMVC) to represent data as a cluster with different categories. Based on multi labeled data, first proposed approach evaluates low dimensional data using optimized matrix factorization (OMF) method and clusters the similar labeled sample data into prototype cluster of dimensional data. After that we represent data in desirable orthonormality constrained view of data using adaptive heuristic to combine complementary data from different dimensions, also provide complexity in computational analysis of data representation. Experimental results of proposed approach applied on high amount of multi view data gives scalable and efficient performance with comparison to traditional multi view related clustering approaches.

Optimal Multi-Tier Clustering of Permissioned Blockchain Systems for IoT

In this work we describe and analyze a novel multi-tier architecture for recording Internet of Things (IoT) data. Each tier in the architecture consists of vertically interconnected node clusters that may be operated by different service providers. Blocks of IoT data are accepted by a cluster tier by tier, until they reach the top tier which adds them to the global blockchain and makes them available to client applications. To allow for large geographical coverage and reduce the dependency on any individual node in the cluster, clusters run a multiple entry version of the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm. We describe an analytical model of the system and find the optimal allocation of clusters and nodes within clusters for a given number of tiers and system coverage area. Our results show that the total number of orderers for given system coverage and total load are main predictors of the block linking time, and indicate that minimizing block linking time is easier to achieve with a smaller number of tiers and large number of clusters with a smaller number of orderers per cluster. These observations can be used as guidelines for planning and dimensioning of multi-tier cluster architectures that create and maintain blockchain structures in a given coverage area.

Multi Clustering Recommendation System for Fashion Retail

Fashion retail has a large and ever-increasing popularity and relevance, allowing customers to buy anytime finding the best offers and providing satisfactory experiences in the shops. Consequently, Customer Relationship Management solutions have been enhanced by means of several technologies to better understand the behaviour and requirements of customers, engaging and influencing them to improve their shopping experience, as well as increasing the retailers’ profitability. Current solutions on marketing provide a too general approach, pushing and suggesting on most cases, the popular or most purchased items, losing the focus on the customer centricity and personality. In this paper, a recommendation system for fashion retail shops is proposed, based on a multi clustering approach of items and users’ profiles in online and on physical stores. The proposed solution relies on mining techniques, allowing to predict the purchase behaviour of newly acquired customers, thus solving the cold start problems which is typical of the systems at the state of the art. The presented work has been developed in the context of Feedback project partially founded by Regione Toscana, and it has been conducted on real retail company Tessilform, Patrizia Pepe mark. The recommendation system has been validated in store, as well as online.

Hybrid Melanoma Classification System Using Multi-Layer Fuzzy C-Means Clustering and Deep Convolutional Neural Network

Skin cancer is considered one of the most common type of cancer in several countries. Due to the difficulty and subjectivity in the clinical diagnosis of skin lesions, Computer-Aided Diagnosis systems are being developed for assist experts to perform more reliable diagnosis. The clinical analysis and diagnosis of skin lesions relies not only on the visual information but also on the context information provided by the patient. Skin lesion segmentation plays a significant part in the earlier and precise identification of skin cancer using computer aided diagnosis (CAD) models. But, the segmentation of skin lesions in dermoscopic images is a difficult process due to the constraints of artefacts (hairs, gel bubbles, ruler markers), unclear boundaries, poor and so on. In this work, multi class skin lesion classification system is developed based on multi layered Fuzzy C-means clustering and deep convolutional neural networks. Evaluate the performance of the proposed MLFCM with DCNN model on multi class skin cancer Dermoscopy images. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.

A mobility‐aware cluster‐based MAC protocol for radio‐ frequency energy harvesting cognitive wireless sensor networks

Cognitive wireless sensor networks (CWSN) are severely energy constrained and radio frequency (RF) wireless energy harvesting (RFWEH) has been shown to improve the network lifetime. In many CWSN applications, node mobility imposes challenges owing to changing network topology. Therefore, the design of a new medium access control (MAC) protocol that can handle node mobility as well as energy harvesting is required. A cluster-based multihop MAC protocol (RMAC-M) is proposed that incorporates RF energy harvesting in a mobility-aware CWSN. Our protocol selects cluster heads using an algorithm based on an R-factor parameter consisting of residual node energy, residual node data and node speed, with appropriate weights. It then transmits data packages using a multitier super cluster head routing mechanism without the need for neighbour discovery. The multitier clustering and RFWEH mechanisms boost the energy performance of the network, increasing its lifetime. On the other hand, time slots allocated for RFWEH increase delay, thereby affecting system latency. Owing to its unique nature, the proposed algorithm has no comparable protocols in the literature. For the sake of completeness, RMAC-M is compared with well-known MAC protocols such as LEACH-M and KoNMAC that do not have energy harvesting or mobility features. Simulation results show that the proposed protocol increases the lifetime of the CWSN nodes substantially, promising a self-sustainable network in terms of energy. Furthermore, despite the allocation of time slots for energy harvesting, critical network parameters such as throughput, packet loss and average delay remain within target levels.

Machine Learning Method for Pancreatic Cancer Detection using Naïve Bayes and Decision Tree Algorithm

Machine Learning is the study of algorithms and application of artificial intelligence. Artificial Intelligence is said to be the superset of machine learning. It aims to develop those programs which can learn and improves it upon increasing experience. It is designed to learn by itself. The aim is to detect pancreatic cancer using machine learning approach. Pancreas is responsible for secreting insulin which helps to control the blood glucose level in the human body. The paper aims to detect pancreatic cancer with the help of machine learning. The tumor is detected using image processing and is to be detected at the premature stage so that proper medication and treatment can be provided to increase the survival rate of the patient. The MRI image of pancreas obtained after MRI scan will be preprocessed and its noise is removed. The segmentation of MRI images will be performed using FCM algorithm. The tumor present in the image will be detected with the help of morphological process and multi clustering model. After Segmentation the image will be divided into various regions. With the help of the hybrid technique the primary and secondary regions are compressed and are used for telemedicine application. DWT is used for DE noising the image. GLCM features are extracted. The image then compared with the database images of pancreatic tumors and is classified as abnormal and normal with the help of BPN based classifier. The image is classified into abnormal and normal. The malignant image is considered as abnormal. The abnormal image is then segmented using SFCM and tumor part is clustered. After clustering the tumor part validation about the presence of pancreatic cancer is given.

Study on microblog public opinion data mining algorithm based on multi-visual clustering model

Because of the random distribution of microblog public opinion data, it is difficult to mine, this paper proposes a microblog public opinion data mining algorithm based on multi vision clustering model. It constructs the phase space distribution structure model of microblog public opinion data, the fuzzy association rule distribution set of microblog public opinion data, analyses the high-order statistical characteristics of microblog public opinion data, and advances the data of fuzzy clustering center according to the different statistical characteristics Row partition block scheduling. The binary structure of microblog public opinion data is reconstructed in the virtual database, and multi angle fuzzy clustering is carried out according to the reconstruction results to realise the optimised mining of microblog public opinion data. The simulation results show that the mining time of this method is up to 4.13 MS and the mining accuracy is up to 100%.

IoT enabled adaptive clustering based energy efficient routing protocol for wireless sensor networks

In this paper, we propose a new internet of things (IoT) enabled multi adaptive clustering (MAC) energy efficient routing protocol for wireless sensor networks (WSNs) to minimise energy dissipation and improve the network performance. This new technique holds the hybrid cluster formation algorithm in which the network topology is divided in two regions where the first region is centralised and the second region is distributed. Both regions contain homogeneous and heterogeneous nodes while the sink is static and located in the centre of both networks. Specifically, proposed IoT enabled MAC routing protocol holds the major three properties: enabling of resources to sensor nodes through IoT, hybrid cluster formation to distribute the network load evenly among sensor nodes and a new mechanism to minimise the energy consumption in long range data transmission. In all of our experiments MAC achieves greater performance than state-of-the-art routing protocols. Moreover, performance evaluation proofs that MAC outperforms in critical network environment.

Multiview Hierarchical Agglomerative Clustering for Identification of Development Gap and Regional Potential Sector

The identification of regional development gaps is an effort to see how far the development conducted in every District in a Province. By seeing the gaps occurred, it is expected that the Policymakers are able to determine which region that will be prioritized for future development. Along with the regional gaps, the identification in Gross Regional Domestic Product (GRDP) sector is also an effort to identify the achievement in the development in certain fields seen from the potential GRDP owned by a District. There are two approaches that are often used to identify the regional development gaps and potential sector, Klassen Typology and Location Quotient (LQ), respectively. In fact, the results of the identification using these methods have not been able to show the proximity of the development gaps between a District to another yet in a same cluster. These methods only cluster the regions and GRDP sectors in a firm cluster based on their own parameter values. This research develops a new approach that combines the Klassen, LQ and hierarchical agglomerative clustering (HAC) into a new method named multi view hierarchical agglomerative clustering (MVHAC). The data of GRDP sectors of 23 Districts in West Java province were tested by using Klassen, LQ, HAC and MVHAC and were then compared. The results show that MVHAC is able to accommodate the ability of the three previous methods into a unity, even to clearly visualize the proximity of the development gaps between the regions and GRDP sectors owned. MVHAC clusters 23 districts into 3 main clusters, they are; Cluster 1 (Quadrant 1) consists of 5 Districts as the members, Cluster 2 (Quadrant 2) consists of 12 Districts and Cluster 3 (Quadrant 4) consists of 6 Districts.

Multi-step service of requests critical to queueing delays in multi-tier systems

Subject of Research.The paper deals with effectiveness analysis of redundant service of requests time-critical to total gradual waiting in the queues of multi-tier clustering nodes. We search for design solutions on the organization of multi-stage redundant service of copies of requests in multi-tier cluster that makes it possible to increase the probability of timely servicing of requests critical to the total waiting time for all system levels (stages of maintenance). Method. We modeled the multi-stage redundant service of copies of requests in multi-tier cluster. The redundancy effectiveness is estimated by the probability of not exceeding the total phased waiting time in queues of nodes for all levels specified for the maximum allowable time. We selected the best options for the organization of redundant requests; the request is considered successful if at least for one accurately executed copy, total waiting time of the stages in the queues of the nodes at all levels of the cluster does not exceed the maximum allowable waiting value. Main Results. The experiments based on calculations carried out with the use of the proposed models on the three-tier cluster example gave the possibility to elucidate the dependence of probability of not exceeding the allowable total request time on the intensity of requests, the multiplicity of redundancy and the maximum allowable cumulative waiting time of requests in the queues of the nodes at all cluster levels. We have shown the existence of efficient redundant service of requests in multi-tier clusters, and the optimal multiplicity of redundancy of request copies depending on the system load and constraints on the allowable total time of staged waiting in the queues of the nodes in the clusters at all levels. Practical Relevance. The proposed models can be used to assess the reliability and timeliness of the service processes that are critical to request waiting time, as well as in the justification of selection of disciplines and service parameters in multi-tier computer systems, including real-time ones.

A Novel Spectral Clustering Based on Nonlinear Low Dimensional Embedding Feature Selection

Spectral clustering is a clustering method based on algebraic graph theory. It has solid theoretical foundation and good performance of clustering. However, during the process of nonlinear low rank approximation, the traditional spectral clustering algorithm can’t effectively remove redundant features leading to the phenomenon that the local area can not distinguish. It also suffers from the high computational complexity of eigen-decomposition when dealing with the high dimensional data. In order to resolve the aforementioned problems, in this paper a novel Spectral clustering algorithm called LF-SC is proposed. Firstly, based on the nonlinear low dimensional embedding feature selection, we realize dimension reduction. The multi clustering structure of the data is captured, the potential manifold structure is fully discovered, and the geometry structure of the low dimensional manifold clustering is well maintained. Secondly, utilizing the SVD instead of EVD to obtain the eigenvectors reduces the computational complexity and maintain the local structure of the data as well as low dimensional manifold. Extensive experiments show the effectiveness and efficiency of our approach.

AN EFFECTIVE MULTI-CLUSTERING ANONYMIZATION APPROACH USING DISCRETE COMPONENT TASK FOR NON-BINARY HIGH DIMENSIONAL DATA SPACES

Abstract Clustering in common is a process of grouping elements together, so that the elements assigned to the same cluster are more comparable to each other than the remaining data points. Certain difficulties related to dealing with high dimensional data are ubiquitous and abundant. Research works conducted using anonymization method for high dimensional data spaces failed to address the problem related to dimensionality reduction for non binary databases. In this paper, Discrete Component Task Specific Multi-Clustering (DCTSM) approach is presented for dimensionality reduction on non binary database. To start with the analysis of attribute in the non binary database takes place and the process of projecting clusters identifies sparseness degree of dimensions. Then with the quantum distribution on multi cluster dimension, the solution for relevancy of attribute and redundancy on non-binary data spaces is provided. As a result, dimensionality reduction on non binary data leads to performance improvement on the basis of tag based feature. Multi clustering tag based feature reduction extracts individual features and are correspondingly replaced by the equivalent feature clusters (i.e.) tag clusters. During training, the DCTSM approach, multi clusters are used instead of the individual tag features and then during decoding the individual features are replaced by the corresponding multi clusters. To measure the effectiveness of the method, experiments are conducted on existing anonymization method for high dimensional data spaces and compared with the DCTSM approach using Statlog German Credit Data Set. DCTSM approach obtained results of 7.05% improved accuracy and was observed that it took minimal time during tag feature extraction and resulted in lesser error rate.

An Effective Multi-clustering Anonymization Approach Using Discrete Component Task for Non Binary High Dimensional Data Spaces

Clustering in common is a process of grouping elements together, so that the elements assigned to the same cluster are more comparable to each other than the remaining data points. Certain difficulties related to dealing with high dimensional data are ubiquitous and abundant. Research works conducted using anonymization method for high dimensional data spaces failed to address the problem related to dimensionality reduction for non binary databases. In this paper, Discrete Component Task Specific Multi-Clustering (DCTSM) approach is presented for dimensionality reduction on non binary database. To start with the analysis of attribute in the non binary database takes place and the process of projecting clusters identifies sparseness degree of dimensions. Then with the quantum distribution on multi cluster dimension, the solution for relevancy of attribute and redundancy on non-binary data spaces is provided. As a result, dimensionality reduction on non binary data leads to performance improvement on the basis of tag based feature. Multi clustering tag based feature reduction extracts individual features and are correspondingly replaced by the equivalent feature clusters (i.e.) tag clusters. During training, the DCTSM approach, multi clusters are used instead of the individual tag features and then during decoding the individual features are replaced by the corresponding multi clusters. To measure the effectiveness of the method, experiments are conducted on existing anonymization method for high dimensional data spaces and compared with the DCTSM approach using Statlog German Credit Data Set. DCTSM approach obtained results of 7.05% improved accuracy and was observed that it took minimal time during tag feature extraction and resulted in lesser error rate.

An Enhanced Detection of Outlier using Independent Component Analysis among Multiple Data Instances via Oversampling

Anomaly is a pattern of data that does not conforms to expected behavior. It is also referred as outlier, exceptions, peculiarities, surprise etc. Anomaly detection aims to identify a small group of instances which deviates from the existing data. It needs to solve an unsupervised yet unstable data learning problem.Detecting an anomaly is an essential research topic in data mining to solve the real world applications like intrusion detection, homeland security to identify the deviated data instances. Mostly anomaly detection methods are implemented in batch mode it requires more computation and memory. Existing system online oversampling Principal Component Analysis (osPCA) algorithm to address this problem and for detecting the presence of outliers from a large amount of data via an online updating technique. In PCA normal data with multi clustering structure and data is in an extremely high dimensional space is not supported. It is typically not easy to use linear models such as PCA to estimate the data distribution if there exists multiple data instances. To overcome these problems and support multiple data instances we proposed a system called Independent Component Analysis (ICA) in which it is a technique of array processing and data analysis aiming at recovering unobserved data samples from oversampled dataset and it is also used to reduce the computation and memory requirement for outlier detection.

Power-aware optimization for heterogeneous multi-tier clusters

Complex web applications are usually served by multi-tier web clusters. With the growing cost of energy, the importance of reducing power consumption in server systems is now well-known and has become a major research topic. However, most existing research focused solely on homogeneous clusters. This paper addresses the challenge of power management in Heterogeneous Multi-tier Web Clusters. We apply Generalized Benders Decomposition (GBD) to decompose the global optimization problem into small sub-problems. This algorithm achieves the optimal solution in an iterative fashion. The evaluation results show that our algorithm achieves more energy conservation than the previous work.

An Energy Efficient Clustering Scheme for Data Aggregation in Wireless Sensor Networks

In wireless sensor networks, a clustering scheme is helpful in reducing the energy consumption by aggregating data at intermediate sensors. This paper discusses the important issue of energy optimization in hierarchically-clustered wireless sensor networks to minimize the total energy consumption required to collect data. We propose a comprehensive energy consumption model for multi-tier clustered sensor networks, in which all the energy consumptions not only in the phase of data transmissions but also in the phase of cluster head rotations are taken into account. By using this new model, we are able to obtain the solutions of optimal tier number and the resulted optimal clustering scheme on how to group all the sensors into tiers by the suggested numerical method. This then enables us to propose an energy-efficiency optimized distributed multi-tier clustering algorithm for wireless sensor networks. This algorithm is theoretically analyzed in terms of time complexity. Simulation results are provided to show that, the theoretically calculated energy consumption by the new model matches very well with the simulation results, and the energy consumption is indeed minimized at the optimal number of tiers in the multi-tier clustered wireless sensor networks.

POINTS CLASSIFICATION BY A SEQUENTIAL HIGHER – ORDER MOMENTS STATISTICAL ANALYSIS OF LIDAR DATA

Abstract. The paper deals with a new sequential procedure to perform unsupervised LIDAR points classification by iteratively studying skewness and kurtosis for elevation and intensity point distribution values. After a preliminary local shape analysis of elevation and intensity point distributions, carried out from the original discrete frequencies by a non parametric estimation of the density functions, the procedure starts by choosing the category of data (elevation or intensity) to analyse at first: the choice falls on the category better showing by a testing procedure a bi or a multi clustering distribution. The first point cluster is identified by studying the distribution skewness and kurtosis variations, after removing at each step the largest data values. The selected cluster is furthermore analysed by studying higher order moments behaviour of the complementary data category. This makes possible to find out potential sub clusters of the original selected one, permitting, in this way, a more effective point classification. Successive clusters are identified by applying the same iterative procedure to the still unclassified LIDAR points. For complex point distribution shapes or for the classification of large areas, a progressive analysis method, based on the partition of the entire data set into regular subsets, is proposed. Some real numerical experiments confirm the capability of the method proposed. The classification total errors in the experiments range from a minimum value of 1,2% to a maximum value of 8,9%.

Efficient Server Provisioning with Control for End-to-End Response Time Guarantee on Multitier Clusters

Dynamic virtual server provisioning is critical to quality-of-service assurance for multitier Internet applications. In this paper, we address three important challenging problems. First, we propose an efficient server provisioning approach on multitier clusters based on an end-to-end resource allocation optimization model. It is to minimize the number of virtual servers allocated to the system while the average end-to-end response time guarantee is satisfied. Second, we design a model-independent fuzzy controller for bounding an important performance metric, the 90th-percentile response time of requests flowing through the multitier architecture. Third, to compensate for the latency due to the dynamic addition of virtual servers, we design a self-tuning component that adaptively adjusts the output scaling factor of the fuzzy controller according to the transient behavior of the end-to-end response time. Extensive simulation results, using two representative customer behavior models in a typical three-tier web cluster, demonstrate that the provisioning approach is able to significantly reduce the number of virtual servers allocated for the performance guarantee compared to an existing representative approach. The approach integrated with the model-independent self-tuning fuzzy controller can efficiently assure the average and the 90th-percentile end-to-end response time guarantees on multitier clusters.