Implementation of the Fuzzy C-Means Clustering Algorithm in Meteorological Data

Lu, Yinghua; Ma, Tinghuai; Yin, Changhong; Xie, Xiaoyu; Tian, Weisheng; Zhong, Shuiming

doi:10.14257/ijdta.2013.6.6.01

Cited by 57 publications

(33 citation statements)

References 20 publications

(20 reference statements)

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“…Using this approach, each value within a series is transferred into an adequate fuzzy state and we obtain the order of states instead of the order of discrete values. For that purpose, we apply the fuzzy C-mean clustering algorithm [28][29][30][31] over the set of daily monitored electricity prices,…”

Section: Fuzzification Of Monitored Datamentioning

confidence: 99%

Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process

et al. 2018

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Abstract:The uncertainty that dominates in the functioning of the electricity market is of great significance and arises, generally, because of the time imbalance in electricity consumption rates and power plants' production capacity, as well as the influence of many other factors (weather conditions, fuel costs, power plant operating costs, regulations, etc.). In this paper we try to incorporate this uncertainty in the electricity price forecasting model by applying interval numbers to express the price of electricity, with no intention of exploring influencing factors. This paper represents a hybrid model based on fuzzy C-mean clustering and the interval-valued autoregressive process for forecasting the short-term electricity price. A fuzzy C-mean algorithm was used to create interval time series to be forecasted by the interval autoregressive process. In this way, the efficiency of forecasting is improved because we predict the interval, not the crisp value where the price will be. This approach increases the flexibility of the forecasting model.

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Section: Fuzzification Of Monitored Datamentioning

confidence: 99%

Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process

et al. 2018

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“…Fuzzy algorithm also performed satisfactory but SOM took the worst time. And hence it is not suitable for computing missing values (Lu, Ma et al 2013). …”

Section: And Hence It Is Not Suitable For Computing Missing Values (Lmentioning

confidence: 99%

Data Cleaning In Data Warehouse: A Survey of Data Pre-processing Techniques and Tools

Fatima¹,

Nazir²,

Khan³

2017

IJITCS

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Abstract-A Data Warehouse is a computer system designed for storing and analyzing an organization's historical data from day-to-day operations in Online Transaction Processing System (OLTP). Usually, an organization summarizes and copies information from its operational systems to the data warehouse on a regular schedule and management performs complex queries and analysis on the information without slowing down the operational systems. Data need to be pre-processed to improve quality of data, before storing into data warehouse. This survey paper presents data cleaning problems and the approaches in use currently for preprocessing. To determine which technique of preprocessing is best in what scenario to improve the performance of Data Warehouse is main goal of this paper. Many techniques have been analyzed for data cleansing, using certain evaluation attributes and tested on different kind of data sets. Data quality tools such as YALE, ALTERYX, and WEKA have been used for conclusive results to ready the data in data warehouse and ensure that only cleaned data populates the warehouse, thus enhancing usability of the warehouse. Results of paper can be useful in many future activities like cleansing, standardizing, correction, matching and transformation. This research can help in data auditing and pattern detection in the data.

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“…It is based on minimizing the criterion with respect to the membership value and the distance d ij [20].…”

Section: C-means Algorithmmentioning

confidence: 99%

Prediction of Stock Market using C-means Clustering and Particle Filter

Darwish¹,

Hilal²

2017

IJCA

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In this article, Particle Filter and C-means are used to predict a value of a point in a time series. Similar data in a time-series are grouped using C-means algorithm. Afterward, a number of particle filters are used as sub-predictors. These sub-predictors start from different points, which are the centers of clusters resulted from clustering algorithm. Outputs from all filters were used to obtain Final prediction result. A weighted average method is used to aggregate the outputs of the filters. Particle filters are used in here to model non-Gaussian time series. Benchmark datasets were used to evaluate the proposed algorithm. To measure its prediction performance, the results derived from the proposed model were compared with those of other algorithms. The comparison proved the effectiveness and accuracy of the proposed method.

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Implementation of the Fuzzy C-Means Clustering Algorithm in Meteorological Data

Abstract: An improved fuzzy c-means algorithm is put forward and applied to deal with

Cited by 57 publications

References 20 publications

Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process

Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process

Data Cleaning In Data Warehouse: A Survey of Data Pre-processing Techniques and Tools

Prediction of Stock Market using C-means Clustering and Particle Filter

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