scholarly journals A qualitative multi-attribute model for the selection of the private hydropower plant investments in Turkey: By foundation of the search results clustering engine (Carrot2), hydropower plant clustering, DEXi and DEXiTree

2016 ◽  
Vol 9 (1) ◽  
pp. 152
Author(s):  
Burak Omer Saracoglu
Keyword(s):  
Electricity Generation ◽  
Clustering Algorithm ◽  
Main Property ◽  
Electricity Demand ◽  
Hydropower Plant ◽  
Data Set ◽  
Search Results ◽  
Private Investors ◽  
Selection Of ◽  
Search Results Clustering

Purpose: The electricity demand in Turkey has been increasing for a while. Hydropower is one of the major electricity generation types to compensate this electricity demand in Turkey. Private investors (domestic and foreign) in the hydropower electricity generation sector have been looking for the most appropriate and satisfactory new private hydropower investment (PHPI) options and opportunities in Turkey. This study aims to present a qualitative multi-attribute decision making (MADM) model, that is easy, straightforward, and fast for the selection of the most satisfactory reasonable PHPI options during the very early investment stages (data and information poorness on projects).Design/methodology/approach: The data and information of the PHPI options was gathered from the official records on the official websites. A wide and deep literature review was conducted for the MADM models and for the hydropower industry. The attributes of the model were identified, selected, clustered and evaluated by the expert decision maker (EDM) opinion and by help of an open source search results clustering engine (Carrot2) (helpful for also comprehension). The PHPI options were clustered according to their installed capacities main property to analyze the options in the most appropriate, decidable, informative, understandable and meaningful way. A simple clustering algorithm for the PHPI options was executed in the current study. A template model for the selection of the most satisfactory PHPI options was built in the DEXi (Decision EXpert for Education) and the DEXiTree software.Findings: The basic attributes for the selection of the PHPI options were presented and afterwards the aggregate attributes were defined by the bottom-up structuring for the early investment stages. The attributes were also analyzed by help of Carrot2. The most satisfactory PHPI options in Turkey in the big options data set were selected for each PHPI options cluster by the EDM evaluations in the DEXi.Originality/value: The recommended DEXi PHPI selection model by the search results clustering engine within a country wise case offered the possibility of easy, meaningful and satisfying continental or worldwide applications for the private investors and the international financial institutions such as the African Development Bank, or the World Bank was the main contribution.

scholarly journals Parallel Implementation of Improved K-Means Based on a Cloud Platform

2019 ◽  
Vol 48 (4) ◽  
pp. 673-681
Author(s):  
Shufen Zhang ◽  
Zhiyu Liu ◽  
Xuebin Chen ◽  
Changyin Luo
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Programming Model ◽  
Parallel Implementation ◽  
Clustering Algorithms ◽  
Data Set ◽  
Large Scale Data ◽  
Sample Density ◽  
Scale Data ◽  
Selection Of

In order to solve the problem of traditional K-Means clustering algorithm in dealing with large-scale data set, a Hadoop K-Means (referred to HKM) clustering algorithm is proposed. Firstly, according to the sample density, the algorithm eliminates the effects of noise points in the data set. Secondly, it optimizes the selection of the initial center point using the thought of the max-min distance. Finally, it uses a MapReduce programming model to realize the parallelization. Experimental results show that the proposed algorithm not only has high accuracy and stability in clustering results, but can also solve the problems of scalability encountered by traditional clustering algorithms in dealing with large scale data.

The Improvement of K-Medoids Clustering Algorithm under Semantic Web

2013 ◽  
Vol 380-384 ◽  
pp. 1286-1289
Author(s):  
Wen Tian Ji ◽  
Qing Ju Guo ◽  
Sheng Zhong
Keyword(s):  
Semantic Web ◽  
Efficient Algorithm ◽  
Clustering Algorithm ◽  
Algorithm Analysis ◽  
Data Set ◽  
Center Point ◽  
Cluster Categories ◽  
Selection Of ◽  
Improved Algorithm

K-medoids clustering algorithm is an efficient algorithm in classifying cluster categories. Based on algorithm analysis, this paper first improves the selection of K center point and then sets up a web model of ontology data set object with the aim of demonstrating through experiment evaluation that the improved algorithm can greatly enhance the accuracy of clustering results under semantic web.

scholarly journals An Experimental Research Study on the Solution of a Private Small Hydropower Plant Investments Selection Problem by ELECTRE III/IV, Shannon’s Entropy, and Saaty’s Subjective Criteria Weighting

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Burak Omer Saracoglu
Keyword(s):  
Experimental Research ◽  
Electricity Generation ◽  
Research Study ◽  
Private Investment ◽  
Shannon’S Entropy ◽  
Hydropower Plant ◽  
Small Hydropower ◽  
Electre Iii ◽  
Shannon's Entropy ◽  
Private Investors

Almost all of the today’s modern daily life conditions of humankind depend on the electricity. The countries either by only themselves or sometimes with some international intuitions and/or organizations have been trying to find the best methods, ways, and projects to supply the electricity to their societies. One of the important tools for the countries to increase the amount and quality of the electricity generation is to activate/ignite/initiate the private investment capabilities/opportunities. The electricity generation market in Turkey is a free/open market for both the foreign and domestic private investors. Hence, both the foreign and domestic private investors have been looking for the most suitable electricity generation plant projects. Small hydropower plant (SHPP) investments (SHPPIs) are one of the alternatives in the Turkish electricity generation market especially for the private investors searching for the renewable energy investments. This experimental research study investigates the possibility of using the ELECTRE III/IV, Shannon’s Entropy, and Saaty’s Analytic Hierarchy Process (AHP) subjective weighting (for criteria) methods for the solution of this problem. In the experimental case study, the most appropriate SHPPIs amongst five alternative SHPPIs at the SHPPIs’ predevelopment investment stages in Turkey were evaluated and ranked in order.

K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

2018 ◽  
Vol 3 (1) ◽  
pp. 001
Author(s):  
Zulhendra Zulhendra ◽  
Gunadi Widi Nurcahyo ◽  
Julius Santony
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Customer Complaints ◽  
Using Data ◽  
Clustering Data ◽  
Service Data ◽  
Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

An approach for document retrieval using cluster-based inverted indexing

2021 ◽  
pp. 016555152110184
Author(s):  
Gunjan Chandwani ◽  
Anil Ahlawat ◽  
Gaurav Dubey
Keyword(s):  
High Performance ◽  
Clustering Algorithm ◽  
Pearson Correlation ◽  
Relevant Information ◽  
Document Retrieval ◽  
Bhattacharyya Distance ◽  
Data Set ◽  
Query Matching ◽  
Inverted Indexing ◽  
Query Optimisation

Document retrieval plays an important role in knowledge management as it facilitates us to discover the relevant information from the existing data. This article proposes a cluster-based inverted indexing algorithm for document retrieval. First, the pre-processing is done to remove the unnecessary and redundant words from the documents. Then, the indexing of documents is done by the cluster-based inverted indexing algorithm, which is developed by integrating the piecewise fuzzy C-means (piFCM) clustering algorithm and inverted indexing. After providing the index to the documents, the query matching is performed for the user queries using the Bhattacharyya distance. Finally, the query optimisation is done by the Pearson correlation coefficient, and the relevant documents are retrieved. The performance of the proposed algorithm is analysed by the WebKB data set and Twenty Newsgroups data set. The analysis exposes that the proposed algorithm offers high performance with a precision of 1, recall of 0.70 and F-measure of 0.8235. The proposed document retrieval system retrieves the most relevant documents and speeds up the storing and retrieval of information.

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