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A Forecasting Model Based On K-means Clustering And Time-invariant Fuzzy Relationship Groups
... for fuzzy time series forecasting [1]-[2], [6], [7] either to find a better forecasting result or to do faster ...of fuzzy time series was proposed by Song and ...introduced ... See full document
7
A New Method For Forecasting Enrolments Combining Time Variant Fuzzy Logical Relationship Groups and K Means Clustering
... many forecasting models have been developed to deal with various problems in order to help people to make decisions, such as crop forecast [7], [8] academic enrolments [2], [11], the temperature prediction [14], ... See full document
8
Forecasting Enrollment Based On The Number Of Recurrences Of Fuzzy Relationships And K-means Clustering
... hybrid forecasting model based on fuzzy time series model with recurrent fuzzy relations and K-mean clustering ...adopting K -mean algorithm, our ... See full document
8
Enrollments Forecasting Based On Aggregated K Means Clustering and Fuzzy Time Series
... for fuzzy forecasting [1]-[2], [6], [7]either to find a better forecasting result or to-do faster ...of fuzzy time series was proposed by Song and ...existing fuzzy ... See full document
7
A Forecasting Model Based On Combining Automatic Clustering Technique And Fuzzy Time Series
... The fuzzy logical relationships and the lengths of intervals are two critical factors that affect forecasting accuracy of ...new forecasting method in the fuzzy time series model ... See full document
6
A Forecasting Method Based on Combining Automatic Clustering Technique and Fuzzy Relationship Groups
... presented based on fuzzy time series(FTS) to forecast real problems, such as forecasting stock market, forecasting enrolments, temperature prediction, ...When forecasting these ... See full document
7
Sugeno-Type Fuzzy Inference Optimization With Firefly and K-Means Clustering Algorithms For Rainfall Forecasting
... Climate Model (GCM), which were used to form a prediction ...long time to compute ...a model based on the minimum and maximum limits, the results of the rainfall forecasting were also ... See full document
14
A Hybrid Forecasting Model Based On Automatic Clustering Algorithm And Fuzzy Time Series
... as forecasting stock market, forecasting enrolments, temperature prediction, population growth prediction, ...used fuzzy time series to handle prediction problems. When forecasting ... See full document
7
A Proposed Model For Forecasting Stock Markets Based On Clustering Algorithm And Fuzzy Time Series
... of fuzzy time series has been widely applied to many various fields such as enrollments, stocks market, weather, population growth prediction and so ...used fuzzy time series to handle ... See full document
6
A New Hybrid Fuzzy Time Series Forecasting Model Combined the Time -Variant Fuzzy Logical Relationship Groups with Particle Swam Optimization
... FTS model. The model was used to forecast stock index and obtained better forecasting ...high-order fuzzy time series model by introducing genetic ...for forecasting the ... See full document
15
High Dimensional Data used in Consensus Neighbour Clustering with Fuzzy Based K-Means and Kernel Mapping
... ABSTRACT: Clustering is the application of data mining techniques to discover patterns from the ...“fuzzy based k-means and kernel mappings with consensus neighbouring clustering ... See full document
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Evaluation Of Fuzzy K-Means And K-Means Clustering Algorithms In Intrusion Detection Systems
... Clustering is a new science that work and study is ongoing in this field because it is considered a lot in different science as a solution. In recent years this method is optimized and the results of optimization ... See full document
7
Study On Fuzzy Time Invariant Series Models For Crop Production Forecasting
... The fuzzy set theory has advanced in a number of ways and in many ...disciplines. Fuzzy set theory has applications in artificial intelligence, computer science, medicine, control engineering, decision ... See full document
13
Study on Fuzzy Time Invariant Series Models for Crop Production Forecasting
... Three fuzzy time series forecasting models applied to this study, the results are almost similar as the average forecasting error of the three models is about ...production forecasting ... See full document
13
Pipe failure prediction in water distribution systems considering static and dynamic factors
... prediction model to assess susceptibility of a pipe to failure is of paramount ...the K-means clustering approach is ...prediction model. To prepare the database for the prediction ... See full document
10
Analysis of Brain Tumor Classification by using Multiple Clustering Algorithms
... and clustering are used to estimate the area of the ...process based on the different algorithms are Fuzzy C-Means, K-Means, Gustafson Kessel algorithm and Density based ... See full document
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Title: APPLICATION OF COLOR BASED IMAGE SEGMENTATION PARADIGM ON RGB COLOR PIXELS USING FUZZY C-MEANS AND K MEANS ALGORITHMS
... of K-means clustering algorithm for image segmentation ...the K-means clustering algorithm for some color spaces, (RGB and LAB color spaces to be compared with this article) find ... See full document
11
A Survey on Deep Feature Learning For Medical Image Analysis for Detection of Brain Tumor
... of Fuzzy C-Means and K-Means Segmentation Algorithm for Iron Detection in Brain SWI using Matlab”, International Journal of Computer Applications (0975 – 8887) Volume 104 – No 15, October ... See full document
5
A Comparative Study of Data Clustering Algorithms
... Effective Clustering Methods for Spatial Data Mining” In this paper, the author(s) developed a new clustering method called CLARANS[8]which is based on randomized ...existing clustering ... See full document
6
Infected fruit part detection using clustering
... used K-Means clustering and Fuzzy C-Means clustering to segment defects in different types of fruit ...of K-Means is that, there may be a skewed clustering ... See full document
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