Data
blogger

blogger

active ARFF Publicly available Visibility: public Uploaded 21-05-2015 by Rafael G. Mantovani
0 likes downloaded by 6 people , 6 total downloads 0 issues 0 downvotes
  • mf_less_than_80 study_123 study_52 study_7 study_88 study_123
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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.

6 features

Class (target)nominal2 unique values
0 missing
V1nominal3 unique values
0 missing
V2nominal3 unique values
0 missing
V3nominal5 unique values
0 missing
V4nominal2 unique values
0 missing
V5nominal2 unique values
0 missing

62 properties

100
Number of instances (rows) of the dataset.
6
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
0
Number of numeric attributes.
6
Number of nominal attributes.
0.05
Average mutual information between the nominal attributes and the target attribute.
First quartile of standard deviation of attributes of the numeric type.
0.6
Average class difference between consecutive instances.
25.27
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1.42
Second quartile (Median) of entropy among attributes.
0.9
Entropy of the target attribute values.
2.83
Average number of distinct values among the attributes of the nominal type.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.06
Number of attributes divided by the number of instances.
Mean skewness among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
18.4
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Mean standard deviation of attributes of the numeric type.
0.08
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
68
Percentage of instances belonging to the most frequent class.
0.58
Minimal entropy among attributes.
Second quartile (Median) of skewness among attributes of the numeric type.
68
Number of instances belonging to the most frequent class.
Minimum kurtosis among attributes of the numeric type.
50
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
2.16
Maximum entropy among attributes.
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
1.8
Third quartile of entropy among attributes.
Maximum kurtosis among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.08
Maximum mutual information between the nominal attributes and the target attribute.
5
The maximum number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
100
Percentage of nominal attributes.
0.08
Third quartile of mutual information between the nominal attributes and the target attribute.
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
0.72
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
Maximum standard deviation of attributes of the numeric type.
32
Percentage of instances belonging to the least frequent class.
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
1.29
Average entropy of the attributes.
32
Number of instances belonging to the least frequent class.
First quartile of means among attributes of the numeric type.
1.17
Standard deviation of the number of distinct values among attributes of the nominal type.
Mean kurtosis among attributes of the numeric type.
3
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
Mean of means among attributes of the numeric type.
First quartile of skewness among attributes of the numeric type.

5 tasks

340 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task