Depth data research of GIS based on clustering analysis algorithm

2018 ◽  
Author(s):  
Yan Xiong ◽  
Wenli Xu
Keyword(s):  
Clustering Analysis ◽  
Analysis Algorithm ◽  
Depth Data

K-Anonymity Algorithm Based on CLIQUE for Green Manufacturing

2013 ◽  
Vol 312 ◽  
pp. 714-718
Author(s):  
Zi Qi Zhao ◽  
Xiao Jun Ye ◽  
Chun Ping Li
Keyword(s):  
Data Processing ◽  
Processing Speed ◽  
Clustering Analysis ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Green Manufacturing ◽  
Multidimensional Data ◽  
Clustering Method ◽  
Analysis Algorithm ◽  
Clique Algorithm

Multidimensional clustering analysis algorithm is for a class of cell-based clustering method of processing speed quickly, time efficiency, mainly to CLIQUE representatives. With time efficient clustering algorithm CLIQUE algorithm can achieve multi-dimensional k - Anonymous the algorithm KLIQUE, KLIQUE algorithm based CLIQUE efficiently retained their CLIQUE algorithm time complexity of features, can play the CLIQUE multidimensional data for the large amount of data processing advantage.

Optimization of Regionalized Wastewater Treatment Systems for the River Ave Basin

1987 ◽  
Vol 19 (9) ◽  
pp. 175-182 ◽  
Author(s):  
António S. Câmara ◽  
João J. de Melo ◽  
David F. Pereira
Keyword(s):  
Wastewater Treatment ◽  
Shortest Path ◽  
Clustering Analysis ◽  
Textile Industry ◽  
Heuristic Methods ◽  
Shortest Path Algorithm ◽  
Analysis Algorithm ◽  
Regional System ◽  
Wastewater Treatment Systems

A new methodology to optimize regionalized wastewater treatment systems is presented. The approach, developed for the River Ave basin, relies upon a clustering analysis algorithm to identify independent sub-sets of polluting sources within the basin. Then for each cluster, heuristic methods are used to generate a network representing the most promising configurations for the regional system, auxiliary models are applied to estimate detailed costs, and a k-shortest path algorithm is used to specify the configurations minimizing cost while achieving a pre-defined efficiency level. To illustrate the proposed method, an application to a sub-section of the basin with seven textile industry units is included.

Big Data Clustering Analysis Algorithm for Internet of Things Based on K-Means

2019 ◽  
Vol 10 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Zhanqiu Yu
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Clustering Analysis ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Prototype System ◽  
Point Selection ◽  
Logistics System ◽  
Relational Schema ◽  
Analysis Algorithm

To explore the Internet of things logistics system application, an Internet of things big data clustering analysis algorithm based on K-mans was discussed. First of all, according to the complex event relation and processing technology, the big data processing of Internet of things was transformed into the extraction and analysis of complex relational schema, so as to provide support for simplifying the processing complexity of big data in Internet of things (IOT). The traditional K-means algorithm was optimized and improved to make it fit the demand of big data RFID data network. Based on Hadoop cloud cluster platform, a K-means cluster analysis was achieved. In addition, based on the traditional clustering algorithm, a center point selection technology suitable for RFID IOT data clustering was selected. The results showed that the clustering efficiency was improved to some extent. As a result, an RFID Internet of things clustering analysis prototype system is designed and realized, which further tests the feasibility.

Application of Clustering Analysis Algorithm in Sorting Stochastic Vibration Environment Measured Data

2011 ◽  
Vol 52-54 ◽  
pp. 1433-1437
Author(s):  
Hai Juan Chang ◽  
Jian Jun Zhang ◽  
Shu Zhu
Keyword(s):  
Clustering Analysis ◽  
Traditional Method ◽  
Measurement Data ◽  
Measured Data ◽  
Inductive Method ◽  
Measurement Technology ◽  
Analysis Algorithm ◽  
Stochastic Vibration ◽  
Vibration Data ◽  
Environment Measurement

Environment measurement technology of aircraft platform is the foundation of environment prediction, while inductive technology of environment measurement data is the support of environment prediction. To analysis how the vibration of different channel and in different position affect the equipment on board, it’s awfully needed to classify the vibration data of aircraft platform according to how was the aircraft flying and where was the sensor placed. However, the traditional method for categorizing gives the sort first, and then make certain if one sample belongs to this category, which is easy to withstand the influence of man-made factors. As it hard to fix the categories first for the vibration on aircraft platform, we put forward using clustering analysis into the categorizing of vibration measured data to eliminate the influence of man-made factors. This method is the improvement of the current inductive method for vibration environment measured data.

Sample Classification Algorithm Based on Spectral Similarity Calculation

2012 ◽  
Vol 490-495 ◽  
pp. 568-572 ◽  
Author(s):  
Ji Qiu Deng ◽  
Lan Qiu ◽  
Qian Hong Wu ◽  
Ying Wang
Keyword(s):  
Cluster Analysis ◽  
Clustering Analysis ◽  
Geological Sample ◽  
Analysis Method ◽  
Geological Samples ◽  
Analysis Algorithm ◽  
Spectral Similarity ◽  
Sample Classification ◽  
Similarity Calculation ◽  
Similarity Computation

According to the field geological sample classification, a spectral similarity the cluster analysis algorithm (SSCA) has been put forward. This algorithm expands and improves the spectra sort encoding algorithm on the spectral sorting and similarity computation, and adds similarity clustering analysis method. Testing on 100 field geological samples using SSCA algorithm, we get the results showing that this algorithm can make further classification for field geological samples to some extent.

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