[PDF] Top 20 A New Method For Forecasting Enrolments Combining Time Variant Fuzzy Logical Relationship Groups and K Means Clustering
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A New Method For Forecasting Enrolments Combining Time Variant Fuzzy Logical Relationship Groups and K Means Clustering
... a new forecasting method in the first-order fuzzy time series model based on the time-variant fuzzy logical relationship groups and K- ... See full document
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Forecasting Enrollment Based On The Number Of Recurrences Of Fuzzy Relationships And K-means Clustering
... a new method for forecasting the enrolments of University of Alabama based on recurrent fuzzy relationship groups and K- means clustering ...apply ... See full document
8
Enrollments Forecasting Based On Aggregated K Means Clustering and Fuzzy Time Series
... a new method for forecasting enrolments based on Fuzzy Time Series and K-Mean ...the enrolments of the University of Alabama are illustrated, and the experimental ... See full document
7
A New Hybrid Fuzzy Time Series Forecasting Model Combined the Time -Variant Fuzzy Logical Relationship Groups with Particle Swam Optimization
... Abstract Fuzzy time series forecasting models are used to overcome traditional time series methods when the historical data of traditional time series approaches contain uncertainty or ... See full document
15
A Forecasting Model Based On Combining Automatic Clustering Technique And Fuzzy Time Series
... for fuzzy forecasting by integrating Chen’s fuzzy forecasting method ...same time, ...high-order fuzzy time series to deal with the enrolments ... See full document
6
A Forecasting Method Based on Combining Automatic Clustering Technique and Fuzzy Relationship Groups
... on fuzzy time series(FTS) to forecast real problems, such as forecasting stock market, forecasting enrolments, temperature prediction, ...When forecasting these problems based on ... See full document
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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
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Context-Based Gustafson-Kessel Clustering with Information Granules
... FCM clustering method guided by a conditional variable, what so called Conditional Fuzzy C-Means (CFCM) ...This clustering estimates the clusters preserving homogeneity of the clustered ... See full document
5
Study and Implementing K mean Clustering Algorithm on English Text and Techniques to Find the Optimal Value of K
... in k-means algorithm. The correct choice of k is often ambiguous; to solve this problem different practitioner used different approaches Elbow method is also one of them to find the right ... See full document
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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
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Segmentation of Medical Images using Adaptively Regularized Kernel based Fuzzy C Means Clustering
... Fuzzy clustering introduces the concept of membership into data partition, for this reason that membership can indicate the degree to which an object belongs to the clusters definitely, and actually ... See full document
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Evaluation Of Fuzzy K-Means And K-Means Clustering Algorithms In Intrusion Detection Systems
... without K- Means which in famous clustering problems are used a ...easy method for clustering a collection of data with specified number of ...of k center for each ...first ... See full document
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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING
... the clustering techniques FCM, SKM and EKM ...in method section that FCM allows pixels to belong to more than one cluster based on degrees of ...SKM clustering algorithm did not produce good ... See full document
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Developing defuzzifying method of fuzzy time-variant series for forecasting product demand
... in forecasting is comparison various methods to determine that which one has the best accuracy or lower MAPE (mean absolute percentage error); hence, another method which is considered to be compared with ... See full document
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Efficient Early Risk Factor Analysis of Kidney Disorder Using Data mining Technique
... C-means clustering (FCM), relies on the basic idea of K-Means, with the difference that in FCM each data point belongs to a cluster to a degree of membership grade, while in ... See full document
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Document Clustering based on the Similarity of Data with Efficient Time Consumption
... Applying genetic algorithms for text mining in searching better document descriptions is used from many years. The final goal is information retrieval researchers define genetic algorithm objective function based on the ... See full document
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Article Forecasting Based on High-Order Fuzzy-Fluctuation Trends and Particle Swarm Optimization Machine Learning Jingyuan Jia 1, Aiwu Zhao 1, * and Shuang Guan 2
... proposed method is acceptable. The greatest advantage of the proposed method is that it put forward a method relying completely on machine learning ...proposed method, they often need to ... See full document
13
Different Feature Selection of Soil Attributes Influenced Clustering Performance on Soil Datasets
... improve clustering performance only if there is a significant relation between environmental va- riables and the soil conditions of ...bad clustering results under SA, SA + SCV, and SA + EV further suggest ... See full document
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LOAD PROFILE CLUSTERING AND FORCASTING OF ELECTRICITY CUSTOMERS
... Data cleaning routines work to “clean” the data by filling in missing values, smoothing noisy data, identifying or removing outliers, and resolving inconsistencies. If users believe the data are dirty, they are unlikely ... See full document
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Efficient Seed and K Value Selection in K Means Clustering Using Relative Weight and New Distance Metric
... partition-based clustering type of algorithms K-means algorithm is the most ...famous. K-means algorithm includes K-means, k-modes and K-Prototypes ... See full document
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