Construction and application of particle swarm optimization algorithm for ecological spatial data mining

2009 ◽  
Author(s):  
ZhongLiang Fu ◽  
Bin Wan
Keyword(s):  
Data Mining ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Spatial Data ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Spatial Data Mining ◽  
Swarm Optimization

The Data Mining Technology of Particle Swarm Optimization Algorithm in Earthquake Prediction

2014 ◽  
Vol 989-994 ◽  
pp. 1570-1573 ◽  
Author(s):  
Xiao Yu Zhang ◽  
Xiang Li ◽  
Xiao Lin
Keyword(s):  
Data Mining ◽  
Particle Swarm Optimization ◽  
Earthquake Prediction ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Earthquake Precursor ◽  
Earthquake Magnitude ◽  
Swarm Optimization ◽  
Mining Technology

Data mining technology based on the particle swarm optimization algorithm applied in earthquake prediction was presented. Making use of the characteristics of abnormally high-dimensional data of earthquake precursor, this paper studies an earthquake prediction model based on the Particle Swarm Optimization Clustering Algorithm. This model analyzes the relationship between earthquake precursor data and earthquake magnitude. Inputs are 14 abnormal indexes such as belt, seismic gap and short leveling, and output is earthquake magnitude classification. The cluster average-distance is set as the evaluation function of the Particle Swarm Optimization Algorithm. The experimental results indicate that, this model can effectively and validly predict the earthquake magnitude in accordance with the earthquake precursor data. Compared with the traditional clustering k-means Algorithm model, this stability is stronger, and the correctness of forecast is much higher. Through the research and analysis of the example of history source seismic data, the model of this paper is one of approaches to improve the efficiency of earthquake forecast.

scholarly journals Cancer Diagnosis with Improved Particle Swarm Optimization Algorithm (Hilla Hospital – Iraq) Case Study

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Zahraa Modher Nabat ◽  
Shaymaa Abdul Hussein Shnain ◽  
Baydaa Jaffer Al Khafaji ◽  
May A. Salih
Keyword(s):  
Data Mining ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Data Mining Techniques ◽  
Improved Particle Swarm Optimization ◽  
The Government ◽  
Using Data

Today, with the improvement and development of technology, hospitals and medical centers produce large amounts of medical data. Given the enormous costs of healthcare services for patients and the government, this data can be used to save on treatment costs using data analysis. Since data mining is a technique to find new knowledge from databases, data mining techniques play an important role in finding and extracting knowledge to contribute to an effective diagnosis of diseases and provision of better medical care and services. In today’s world, due to the high incidence of cancer and its high mortality rate, early diagnosis of this disease is of great interest. In this paper, we tried using data mining techniques such as classification and improved particle swarm optimization algorithm to detect cancer types in the shortest time, with more details, and provide them to the physician.

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