27,350 research outputs found
Two-layer classification and distinguished representations of users and documents for grouping and authorship identification
Most studies on authorship identification reported a drop in the identification result when the number of authors exceeds 20-25. In this paper, we introduce a new user representation to address this problem and split classification across two layers. There are at least 3 novelties in this paper. First, the two-layer approach allows applying authorship identification over larger number of authors (tested over 100 authors), and it is extendable. The authors are divided into groups that contain smaller number of authors. Given an anonymous document, the primary layer detects the group to which the document belongs. Then, the secondary layer determines the particular author inside the selected group. In order to extract the groups linking similar authors, clustering is applied over users rather than documents. Hence, the second novelty of this paper is introducing a new user representation that is different from document representation. Without the proposed user representation, the clustering over documents will result in documents of author(s) distributed over several clusters, instead of a single cluster membership for each author. Third, the extracted clusters are descriptive and meaningful of their users as the dimensions have psychological backgrounds. For authorship identification, the documents are labelled with the extracted groups and fed into machine learning to build classification models that predicts the group and author of a given document. The results show that the documents are highly correlated with the extracted corresponding groups, and the proposed model can be accurately trained to determine the group and the author identity
The Five Factor Model of personality and evaluation of drug consumption risk
The problem of evaluating an individual's risk of drug consumption and misuse
is highly important. An online survey methodology was employed to collect data
including Big Five personality traits (NEO-FFI-R), impulsivity (BIS-11),
sensation seeking (ImpSS), and demographic information. The data set contained
information on the consumption of 18 central nervous system psychoactive drugs.
Correlation analysis demonstrated the existence of groups of drugs with
strongly correlated consumption patterns. Three correlation pleiades were
identified, named by the central drug in the pleiade: ecstasy, heroin, and
benzodiazepines pleiades. An exhaustive search was performed to select the most
effective subset of input features and data mining methods to classify users
and non-users for each drug and pleiad. A number of classification methods were
employed (decision tree, random forest, -nearest neighbors, linear
discriminant analysis, Gaussian mixture, probability density function
estimation, logistic regression and na{\"i}ve Bayes) and the most effective
classifier was selected for each drug. The quality of classification was
surprisingly high with sensitivity and specificity (evaluated by leave-one-out
cross-validation) being greater than 70\% for almost all classification tasks.
The best results with sensitivity and specificity being greater than 75\% were
achieved for cannabis, crack, ecstasy, legal highs, LSD, and volatile substance
abuse (VSA).Comment: Significantly extended report with 67 pages, 27 tables, 21 figure
ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise for Identifying Clusters with Varying Densities
Density-based spatial clustering of applications with noise (DBSCAN) is a
data clustering algorithm which has the high-performance rate for dataset where
clusters have the constant density of data points. One of the significant
attributes of this algorithm is noise cancellation. However, DBSCAN
demonstrates reduced performances for clusters with different densities.
Therefore, in this paper, an adaptive DBSCAN is proposed which can work
significantly well for identifying clusters with varying densities.Comment: To be published in the 4th IEEE International Conference on
Electrical Engineering and Information & Communication Technology (iCEEiCT
2018
Typical Phone Use Habits: Intense Use Does Not Predict Negative Well-Being
Not all smartphone owners use their device in the same way. In this work, we
uncover broad, latent patterns of mobile phone use behavior. We conducted a
study where, via a dedicated logging app, we collected daily mobile phone
activity data from a sample of 340 participants for a period of four weeks.
Through an unsupervised learning approach and a methodologically rigorous
analysis, we reveal five generic phone use profiles which describe at least 10%
of the participants each: limited use, business use, power use, and
personality- & externally induced problematic use. We provide evidence that
intense mobile phone use alone does not predict negative well-being. Instead,
our approach automatically revealed two groups with tendencies for lower
well-being, which are characterized by nightly phone use sessions.Comment: 10 pages, 6 figures, conference pape
Experiences in Mining Educational Data to Analyze Teacher's Performance: A Case Study with High Educational Teachers
Educational Data Mining (EDM) is a new paradigm aiming to mine and extract
knowledge necessary to optimize the effectiveness of teaching process. With normal
educational system work itās often unlikely to accomplish fine system optimizing due to
large amount of data being collected and tangled throughout the system. EDM resolves
this problem by its capability to mine and explore these raw data and as a consequence of
extracting knowledge. This paper describes several experiments on real educational data
wherein the effectiveness of Data Mining is explained in migration the educational data
into knowledge. The experiments goal at first to identify important factors of teacher
behaviors influencing student satisfaction. In addition to presenting experiences gained
through the experiments, the paper aims to provide practical guidance of Data Mining
solutions in a real application
- ā¦