A hybrid clustering and classification technique for soil data mining

2007 ◽  
Author(s):  
L. Vibha ◽  
G.M.H. Vardhan ◽  
S.J. Prashanth ◽  
P.D. Shenoy ◽  
K.R. Venugopal ◽  
...  
Keyword(s):  
Data Mining ◽  
Hybrid Clustering ◽  
Classification Technique ◽  
Clustering And Classification

scholarly journals A Hybrid clustering and classification technique for forecasting short-term energy consumption

2018 ◽  
Vol 38 (1) ◽  
pp. 66-76 ◽  
Author(s):  
Mehrnoosh Torabi ◽  
Sattar Hashemi ◽  
Mahmoud Reza Saybani ◽  
Shahaboddin Shamshirband ◽  
Amir Mosavi
Keyword(s):  
Energy Consumption ◽  
Short Term ◽  
Hybrid Clustering ◽  
Classification Technique ◽  
Clustering And Classification ◽  
Term Energy

scholarly journals A Data Mining Technique for Tourist Destination Brand Image Building

2020 ◽  
Vol 8 (6) ◽  
pp. 4617-4622
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Tourism Industry ◽  
Destination Image ◽  
Data Mining Technique ◽  
Tourist Destination ◽  
Text Data ◽  
Clustering And Classification ◽  
Google Search

The destination image branding is the domain of tourism industry where the facts and information is collected and evaluated for finding the credibility of a target tourist destination. Manual collection and processing of collected information accurately is a complicated and time consuming task therefore a data mining model is suggested ,in this presented work that collect and evaluate the destination image accurately and based on evaluation can make the recommendations about visits of tourist. In order to perform this task data mining techniques are applied on text data source. In first the data is extracted from the Google search engine and it is preprocessed for make it impure. In further the data is labeled based on the positive and negative words available in the collected facts. Finally the clustering and classification of text is performed. For clustering of data FCM (fuzzy c means) clustering algorithm and for classification the Bayesian classifier is used. Based on final classification of text data the decision is made for the destination visits.

Institutional Research Using Data Mining

2011 ◽  
pp. 66-102 ◽  
Author(s):  
Constanta-Nicoleta Bodea ◽  
Vasile Bodea ◽  
Radu Mogos
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Virtual Learning Environment ◽  
Student Activities ◽  
Participatory Behavior ◽  
Student Profiles ◽  
Clustering And Classification ◽  
E Learning ◽  
Using Data ◽  
And Behavior

The aim of this chapter is to explore the application of data mining for analyzing academic performance in connection with the participatory behavior of the students enrolled in an online two-year Master degree program in project management. The main data sources were the operational database with the students’ records and the log files and statistics provided by the e-learning platform. One hundred eighty-one enrolled students, and more than 150 distinct characteristics/ variables per student were used. Due to the large number of variables, an exploratory data analysis through data mining was chosen, and a model-based discovery approach was designed and executed in Weka environment. The association rules, clustering, and classification were applied in order to identify the factors explaining the students’ performance and the relationship between academic performance and behavior in the virtual learning environment. Data mining has revealed interesting patterns in data. These patterns indicate that academic performance is related to the intensity of the student activities in virtual environment. If the student understands how to work and she/he is motivated to communicate with others, then he might have a good academic performance. Based on clustering analysis, different student profiles were discovered, explaining the academic performance. The results are very encouraging and suggest several future developments.

A Model for Extracting Most Desired Web Pages

2020 ◽  
pp. 119-145
Author(s):  
Jayanti Mehra ◽  
Ramjeevan Singh Thakur
Keyword(s):  
Data Mining ◽  
Path Length ◽  
Web Mining ◽  
Statistical Information ◽  
Web Pages ◽  
Systems Practice ◽  
Clustering And Classification ◽  
Using Data ◽  
Access Logs ◽  
Manipulation Process

Weblog analysis takes raw data from access logs and performs study on this data for extracting statistical information. This info incorporates a variety of data for the website activity such as average no. of hits, total no. of user visits, failed and successful cached hits, average time of view, average path length over a website; analytical information such as page was not found errors and server errors; server information, which includes exit and entry pages, single access pages, and top visited pages; requester information like which type of search engines is used, keywords and top referring sites, and so on. In general, the website administrator uses this kind of knowledge to make the system act better, helping in the manipulation process of site, then also forgiving marketing decisions support. Most of the advanced web mining systems practice this kind of information to take out more difficult or complex interpretations using data mining procedures like association rules, clustering, and classification.

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