Oral cancer detection using data mining tool

2017 ◽  
Author(s):  
Arushi Tetarbe ◽  
Tanupriya Choudhury ◽  
Teoh Teik Toe ◽  
Seema Rawat
Keyword(s):  
Data Mining ◽  
Oral Cancer ◽  
Cancer Detection ◽  
Data Mining Tool ◽  
Mining Tool ◽  
Using Data

The Research on C2C E-Commerce Website Using Data Mining Tools to Improve the Network Marketing Effect

2013 ◽  
Vol 433-435 ◽  
pp. 1885-1889
Author(s):  
Lu Feng ◽  
Zhan Quan Wen ◽  
Jie Mei Lin
Keyword(s):  
Data Mining ◽  
Analysis Method ◽  
Network Marketing ◽  
Data Mining Tool ◽  
Mining Tool ◽  
Using Data ◽  
Object Of Study ◽  
Hyperlink Analysis ◽  
Mining Tools

We used the principle of hyperlink analysis method to mine the website data according to the indicators of the hyperlink analysis. We selected Taobao.com as an object of study. The evaluation indicators of network marketing effect were page views, sales quantity, sales, the number of adding store to bookmark . According to our research, we find Taobao.com stores can use data mining tool to obtain the very good marketing effect.

scholarly journals Effectively Optimizing the Patterns with Dynamic Behaviors of the Transactions by using Data Mining Tool

2019 ◽  
Vol 8 (12) ◽  
pp. 2312-2316
Keyword(s):  
Data Mining ◽  
Reaction Time ◽  
Market Basket ◽  
Data Mining Tool ◽  
Testing Issue ◽  
Mining Tool ◽  
Test Investigation ◽  
Using Data ◽  
The Right ◽  
Time Information

Basic management and understanding the conducted of the client has turned out to be indispensable and testing issue for associations to continue their situation in the focused markets. Mechanical advancements have cleared leap forward in quicker handling of questions and sub-second reaction time. Information mining devices have turned out to be surest weapon for breaking down colossal measure of information and leap forward in settling on right choices. The target of this paper is to break down the colossal measure of information subsequently abusing the buyer conduct and settle on the right choice prompting aggressive edge over adversaries. Test investigation has been done utilizing affiliation principles utilizing Market Basket examination toward demonstrate its value more the regular systems.

scholarly journals Youtube spam detection framework using naïve bayes and logistic regression

2019 ◽  
Vol 14 (3) ◽  
pp. 1508
Author(s):  
Nur’Ain Maulat Samsudin ◽  
Cik Feresa binti Mohd Foozy ◽  
Nabilah Alias ◽  
Palaniappan Shamala ◽  
Nur Fadzilah Othman ◽  
...  
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Naive Bayes ◽  
Research Result ◽  
Naïve Bayes ◽  
Spam Detection ◽  
Features Selection ◽  
Data Mining Tool ◽  
Mining Tool ◽  
Using Data

YouTube has become a popular social media among the users. Due to YouTube popularity, it became a platform for spammer to distribute spam through the comments on YouTube. This has become a concern because spam can lead to phishing attack which the target can be any user that click any malicious link. Spam has its own features that can be analyzed and detected by classification. Hence, enhancement features are proposed to detect YouTube spam. In order to conduct the experiments, a YouTube Spam detection framework that consists of five (5) phases such as data collection, pre-processing, features selection and extraction, classification and detection were developed. This paper, proposed the YouTube detection framework, examined and validate each of the phases by using two types of data mining tool. The features are constructed from analysis by using data collected from YouTube Spam dataset by using Naïve Bayes and Logistic Regression and tested in two different data mining tools which is Weka and Rapid Miner. From the analysis, thirteen (13) features that had been tested on Weka and RapidMiner shows high accuracy, hence is being used throughout the experiment in this research. Result of Naïve Bayes and Logistic Regression run in Weka is slightly higher than RapidMiner. In addition, result of Naïve Bayes is higher than Logistic Regression with 87.21% and 85.29% respectively in Weka. While in RapidMiner there is slightly different of accuracy between Naïve Bayes and Logistic Regression 80.41% and 80.88%. But, precision of Naïve Bayes is higher than Logistic Regression.

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