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检索条件"主题词=K-means clustering algorithm"

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Blood Bank clustering: Improving Performance of clustering using Entropy Weighted k-means

Blood Bank Clustering: Improving Performance of Clustering u...

引用

IEEE International Conference on Intelligent Systems, Smart and Green Technologies (ICISSGT)

作者： Srinivas, M. Satya Lakshmi, P. Vijaya kumar, V. kalyan Durga Shyam Balaji, V. Siva Sai Sasi Inst Tech & Engn Comp Sci & Engn Tadepalligudem AP India

ISBN: (纸本)9781665404167

This research provides a novel method for improving clustering efficiency in blood banks. Blood transfusions are used to save the lives of billions of people throughout the world. Every day, about 2 million people are involved in accidents throughout the world, resulting in an emergency scenario in which we require adequate blood for the patient to cover up the blood loss and ensure his survival. Blood banks play a significant role in this situation;a blood bank is a spot where blood is collected and stored for future use;nevertheless, locating a suitable blood bank in the surrounding area is a common task for patient caretakers. In this research, we presented an effective clustering or grouping of blood banks to tackle this problem. Our primary objective here is to first cluster blood banks using an advanced version of the k means clustering algorithm, namely the Entropy weighted k means clustering algorithm while taking into account factors such as longitudinal and latitudinal points of blood banks, blood bank category (Government, Charity, and Private), and blood component availability. We use the sum of the square error to assess the effectiveness of this clustering algorithm. In this process, we also identify the region where a new blood bank is needed based on longitudinal and latitudinal information.

关键词： Blood Banks Entropy Weighted k-means clustering algorithm Machine Learning Data Analysis

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Outliers Detection Method Using clustering in Buildings Data 41

Outliers Detection Method Using Clustering in Buildings Data

引用

41st Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Habib, Usman Zucker, Gerhard Bloechle, Max Judex, Florian Haase, Jan AIT Austrian Inst Technol Dept Energy Vienna Austria Helmut Schmidt Univ Hamburg Hamburg Germany

ISBN: (纸本)9781479917624

To achieve energy efficiency in buildings, a lot of raw data is recorded, during the operation of buildings. This recorded raw data is further used for the analysis of the performance of buildings and its different components e.g. Heating, Ventilation and Air-Conditioning (HVAC). To save time and energy it is required to ensure resilience of the data by detecting and replacing outliers (i.e. data samples that are not plausible) in the data before detailed analysis. This paper discusses the steps involved for detecting outliers in the data obtained from absorption chiller using their On/Off state information. It also proposes a method for automatic detection of On/Off and/or Missing Data status of the chiller. The technique uses two layer k-means clustering for detecting On/Off as well as Missing Data state of the chiller. After automatic detection of the chiller On/Off cycle, a method for outlier detection is proposed using Z-Score normalization based on the On/Off cycle state of chillers and clustering outliers by Expectation Maximization clustering algorithm. Moreover, the results of filling the missing values with regression and linear interpolation for short and long periods are elaborated. All proposed methods are applied to real building data and the results are discussed.

关键词： adsorption chillers physical rules k-means clustering algorithm Outliers Z-Score Normalization Expectation Maximization clustering algorithm (EM) Heating Ventilation and Air-Conditioning(H-VAC) Fault detection and diagnosis (FDD) Machine learning regression linear interpolation

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Establishment and Implementation of Securities Company Customer Classification Model Based on clustering Analysis and PCA

Establishment and Implementation of Securities Company Custo...

引用

International Conference on Control Engineering and Communication Technology (ICCECT)

作者： Liu Bin Qiu Huayong Shen Yizhen Chinese Acad Sci Grad Univ Ind Secur Stock Ltd Co Fuzhou Peoples R China Ind Secur Stock Ltd Co Fuzhou Peoples R China

ISBN: (纸本)9781467344999;9780769548814

In the paper, advanced data mining technology is applied to the analysis of customers' historical exchange data in the customer classification model of securities exchange. The accuracy and effectiveness of the results is remarkably improved. The process involves using data warehouse to achieve the storage of massive customer transaction data, the construction of the fundamental indicator system, the selection of the indicators using PCA and the construction of the customer classification model using k-means clustering algorithm. The application of these technologies significantly improves the accuracy and effectiveness the customer classification indicators, enabling the results closer to the mettle of the customers and basically solving the key points and difficulties in the suitability management.

关键词： securities data mining k-means clustering algorithm principal component analysis PCA customer classification model

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Discrete phase CV-QkD over a hybrid free space - fibre channel with k-means clustering

Discrete phase CV-QKD over a hybrid free space - fibre chann...

引用

Conference on Quantum Technologies

作者： Vilashini, Saravana M. Prabhakara, Anil Indian Inst Technol Madras Chennai 600036 India

ISBN: (纸本)9781510673052;9781510673045

Quadrature phase shift keying continuous variable quantum key distribution (QPSk CV-QkD) can be used to encode bits of information on weak coherent states, enhanced with quantum scissors. In our experiment, we use discretely modulated QPSk CV-QkD across a 150 m long free-space optical (FSO) link over the two buildings with a four symbol constellation. Other CV-QkD deployments in the literature have demonstrated a Frame Error Rate of 20% with a 100 MHz repetition rate and a loss of 6 dB in the channel. Our setup has a QBER of 25% for a 35 dB channel loss. Our system has been tested for a link loss of upto 50 dB. The QPSk symbols generated at 100 MHz from a Zynq Ultrascale+ Field Programmable Gated Array and fed to an I/Q transmitter, which in turn modulated a 1550 nm laser. The FSO transmitter, connected to our lab 500 m away, consists of a collimator and a beam expander. The transmitted states were captured on the other side by a FSO receiver unit consisting of a collimator. The received signal was transported back to the lab via a fiber of length 1 km, which is then passed through a 90 degree hybrid coherent receiver, with a 1 GHz bandwidth. The captured quadratures are post processed with a k-means cluster algorithm allowing us to recover the bit values. Our experiments focus on understanding the change in BER in the presence of varying levels of attenuation over the hybrid FSO+fibre channel.

关键词： Quadrature phase shift keying Free Space Optics Continuous Variable Quantum key Distribution k-means clustering algorithm

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An Ontology Learning Method Based on Document clustering

An Ontology Learning Method Based on Document Clustering

引用

2nd International Conference on Frontiers of Manufacturing and Design Science (ICFMD 2011)

作者： Wei, Xianmin Weifang Univ Comp & Commun Engn Sch Weifang Peoples R China

ISBN: (纸本)9783037852828

Ontology learning is a series method and technology of semi-automatic ontology construction, which uses various data sources to create or expand in-built ontology by semi-automatic method to build a new ontology. Existing ontology construction methods are to collect a large number of conceptual terms based on a large number of field text and background corpus, and then to select field concepts to construct a body. The proposed Cluster-Merge algorithm is to use k-means clustering algorithm in the field document at first, then according to document clustering results to construct body by themself, at last accoring to the ontology similarity for ontology merging to get final output ontology. The experiment may prove that Cluster-Merge algorithm can improve the body resulting recall and precision.

关键词： ontology learning document clustering k-means clustering algorithm similarity ontology merging

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Improved clustering algorithm of Neighboring Reference Points Based on kNN for Indoor Localization 5

Improved Clustering Algorithm of Neighboring Reference Point...

引用

5th IEEE Conference on Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS)

作者： Xue, Weixing Hua, Xianghong Li, Qingquan Qiu, Weining Peng, Xuesheng Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Hubei Peoples R China Shenzhen Univ Dept Shenzhen Key Lab Spatial Smart Sensing & Ser Shenzhen 518060 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Hubei Peoples R China

ISBN: (纸本)9781538637555

Reference points (RP) clustering methods such as k-means are frequently used to reduce the region of search in most of fingerprint clustering algorithms. However, traditional clustering algorithms analysis the geometric proximity of RP only in the offline phase, which has nothing to do with the test point. Meanwhile, both the clustering pattern and the number of clusters need to be predefined directly or indirectly, which means an unsuitable clustering pattern or an unsuitable number of clusters would lead to poor estimation accuracy. In this letter, in order to improve the performance of kNN algorithm with the neighboring RP selection, we utilize the k-means clustering algorithm to analysis the geometric proximity between RP and test point in the online phase. In the proposed algorithm, k-means clustering algorithm groups M (M<k) nearest neighboring RPs according to their real physical distances to the test point instead of their signal distances. Then these M nearest neighboring RPs are used to estimate the location of the test point. Experiments were conducted in the fourteenth floor within an office building and the results demonstrate that the proposed method considerably outperforms the kNN algorithms in terms of positioning accuracy.

关键词： reference points clustering method geometric proximity physical real distance k-means clustering algorithm indoor location

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k-means clustering for Adaptive Wavelet Based Image Denoising 2

K-Means Clustering for Adaptive Wavelet Based Image Denoisin...

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International Conference on Advances in Computer Engineering and Applications (ICACEA)

作者： Agrawal, Utkarsh Roy, Soumava kumar Tiwary, U. S. Prashanth, D. S. IIIT Allahabad Dept Informat Technol Allahabad Uttar Pradesh India

ISBN: (纸本)9781467369114

clustering algorithms are used for systematic retrieval of data by organizing them into several clusters. k-means is one such algorithm which partitions data into groups based on distance metric in an unsupervised way. clustering is used to organize data for efficient retrieval. In this paper, we study Denoising of images corrupted with variable Gaussian noise spread across the images (dataset). The dataset was made by applying k-means grouping statistical parameters of the training images which are present in wavelet domain. Adaptive Soft thresholding of noisy images is done, selecting the best parameter based on the cluster. After applying inverse wavelet transform PSNR of the denoised image is calculated. Impressive results are obtained by applying this technique.

关键词： k-means clustering algorithm Image Denoising Soft Thresholding Discrete Wavelet Transform

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fMRI segmentation at 1.5T by clustering

fMRI segmentation at 1.5T by clustering

引用

SPIE Medical Imaging 1995 Conference - Physiology and Function from Multidimensional Images

作者： kIM, T kHOSLA, D PATEL, P SINGH, M UNIV SO CALIF DEPT BIOMED ENGNLOS ANGELESCA 90089

ISBN: (纸本)0819417815

A feasibility study was conducted to segment 1.5T fMRIs into gray matter and large veins using individual pixel intensity and temporal phase delay as two correlated parameters in gradient echo images. The time-course of each pixel in gradient echo images acquired during visual stimulation with a checkerboard flashing at 8Hz was correlated to the stimulation 'on'-'off' sequence to identify activated pixels. The temporal delay of each activated pixels was estimated by fitting its time-course to a reference sinusoidal function. The mean signal intensity difference of the activated pixels was computed by subtracting the average of the 'on' images from the average of the 'off' images. After replacing each activated pixel with 2D features (i.e., intensity and time-delay), a clustering method based on a k-means algorithm was employed to classify vein and tissue pixels. Good demarcation between large veins and activated gray matter was achieved with this method.

关键词： FUNCTIONAL MAGNETIC RESONANCE IMAGING (FMRI) SEGMENTATION VEINS BRAIN PARENCHYMA k-means clustering algorithm

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Data clustering Approaches Survey and Analysis 1

Data Clustering Approaches Survey and Analysis

引用

International Conference On Futuristic Trends in Computational Analysis and knowledge Management

作者： Ahalya, G. Pandey, Hari Mohan Amity Univ ASET CSE Dept Noida India

ISBN: (纸本)9781479984336

In the current world, there is a need to analyze and extract information from data. clustering is one such analytical method which involves the distribution of data into groups of identical objects. Every group is known as a cluster, which consists of objects that have affinity within the cluster and disparity with the objects in other groups. This paper is intended to examine and evaluate various data clustering algorithms. The two major categories of clustering approaches are partition and hierarchical clustering. The algorithms which are dealt here are: k-means clustering algorithm, hierarchical clustering algorithm, density based clustering algorithm, self-organizing map algorithm, and expectation maximization clustering algorithm. All the mentioned algorithms are explained and analyzed based on the factors like the size of the dataset, type of the data set, number of clusters created, quality, accuracy and performance. This paper also provides the information about the tools which are used to implement the clustering approaches. The purpose of discussing the various software/tools is to make the beginners and new researchers to understand the working, which will help them to come up with new product and approaches for the improvement.

关键词： clustering Expectation maximization clustering algorithm Hierarchical clustering k-means clustering algorithm Self-organization maps algorithm density based clustering algorithm

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Path Planning Based on clustering and Improved ACO in UAV-assisted Wireless Sensor Network

Path Planning Based on Clustering and Improved ACO in UAV-as...

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IEEE International Symposium on Antennas and Propagation (IEEE AP-S) / IEEE USNC-CNC-URSI Radio Science Meeting

作者： Li, Yibing Meng, Xianzhen Ye, Fang Jiang, Tao Li, Yingsong Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China

ISBN: (纸本)9781946815088

UAVs as mobile nodes have been introduced into wireless sensor network (WSN) to assist information transmission and reduce the burden of communication. To minimize the path cost of UAVs for information transmission, this paper focuses on the path planning of UAVs. A multi-UAVs path planning combined k-means clustering algorithm and improved MAX-MIN ant system (MMAS) is proposed. This algorithm apply the k-means clustering algorithm to reduce the problem size and improve the search efficiency of subsequent path planning. By modifying the node search rules and proposing two optimal solution detection rules of the MMAS, the algorithm searching stagnation and failing into local optimal solution are effectively avoided.

关键词： UAV k-means clustering algorithm path planning MAX-MIN ant system

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