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Author:
Source: UCI
Please cite:
Source:
http://www.ijcaonline.org/archives/volume47/number18/7291-0509
Data Set Information:
In this paper, we look for to recognize the causes of users tend to cyber space in Kohkiloye and Boyer Ahmad Province in Iran. Collecting information to form database is done by questionnaire. This questionnaire is provided as oral, written and also programming of a website which includes an internet questionnaire and the users can answer the questions as they wish. They entered their used websites, blogs and social networks during the day. After collecting questionnaires, the wed addresses are gathered to get expected results. And finally, their trustfulness is checked by analyzing their used web pages. As the results were same, for getting better and noiseless response, they will put in database.
Attribute Information:
We considered the following parameters as questions: age, education, political attitudes, blog topic, and the type of the identity in internet, the influence of manager inefficiency on tendency, the effect of inefficient media on tendency, the effects of social and political conditions on tendency and finally the effect of poverty in the province on tendency. The noisy or too detailed data in database makes us far from to get proper and suitable answers of algorithms [8]. We preprocessed the data and eliminated some non-relevant data. Finally the followings are considered as the main fields which include: education, political caprice, topics, local media turnover (LMT) and local, political and social space (LPSS).
The collected data are shown in Table 1. In order to get correct answer, we classify bloggers to two groups: professional bloggers and seasonal (temporary) bloggers. Professional bloggers are those who adopt blog as an effective digital media and interested in digital writing in continuous time intervals. Seasonal (temporary) bloggers are not professional and follow blogging in discrete time periods. In this study, we review the tendency factors considering whether these people are among professional bloggers (Pro Bloggers, PB) and then, consider the other factors according to it.

Class (target) | nominal | 2 unique values 0 missing | |

V1 | nominal | 3 unique values 0 missing | |

V2 | nominal | 3 unique values 0 missing | |

V3 | nominal | 5 unique values 0 missing | |

V4 | nominal | 2 unique values 0 missing | |

V5 | nominal | 2 unique values 0 missing |

First quartile of standard deviation of attributes of the numeric type.

0.51

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2

0.34

Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.1

Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Second quartile (Median) of kurtosis among attributes of the numeric type.

0.13

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2

0.51

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3

0.58

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump

0.08

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

0.33

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump

Second quartile (Median) of skewness among attributes of the numeric type.

0.13

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3

0.13

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump

0

Minimal mutual information between the nominal attributes and the target attribute.

Second quartile (Median) of standard deviation of attributes of the numeric type.

0.82

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

0.08

Maximum mutual information between the nominal attributes and the target attribute.

2

The minimal number of distinct values among attributes of the nominal type.

0.2

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

18.4

Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

5

The maximum number of distinct values among attributes of the nominal type.

0.49

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

0.57

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001

0.56

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.82

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

0.08

Third quartile of mutual information between the nominal attributes and the target attribute.

0.34

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.2

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

0.1

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001

0.73

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes

0.1

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.49

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

0.57

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

Third quartile of standard deviation of attributes of the numeric type.

0.56

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.82

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

0.51

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1

0.34

Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.2

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

0.05

Average mutual information between the nominal attributes and the target attribute.

0

First quartile of mutual information between the nominal attributes and the target attribute.

0.1

Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.49

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

0.57

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001

25.27

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

0.13

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1

0.56

Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

1.17

Standard deviation of the number of distinct values among attributes of the nominal type.

2.83

Average number of distinct values among the attributes of the nominal type.