We investigate a novel approach for intuitive interaction with a data set for explorative data analysis. The key idea is that a user can directly interact with a two or three dimensional embedding of the data and actively place data points to desired locations. To achieve this, we propose a variant of semisupervised kernel PCA which respects the placement of control points and maximizes the var...

We present a newly collected data set of 8,868 gold-standard annotated Arabic twitter feeds. The corpus is manually labelled for subjectivity and sentiment analysis (SSA) (κ = 0.816). In addition, the corpus is annotated with a variety of linguistically motivated feature-sets that have previously shown positive impact on classification performance. The paper highlights issues posed by twitter a...

Semi-continuous data arise in many applications where naturally-continuous data become contaminated by the data generating mechanism. The resulting data contain several values that are “too frequent”, and in that sense are a hybrid between discrete and continuous data. The main problem is that standard statistical methods, which are geared towards continuous or discrete data, cannot be applied ...

One way to look at basic modeling approaches is to split them up into mechanistic and data based models. A few years ago we developed our own data based model approach [1], called Genetic Algorithm driven Clustering (GAdC). The proposed methodology relies on semisupervised clustering with a generative floating-point genetic algorithm and local learning. In this contribution we investigate the i...

In previous work, semi-supervised Fuzzy c-means (ssFCM) was used as an automatic classification technique to classify the Nottingham Tenovus Breast Cancer (NTBC) dataset as no method to do this currently exists. However, the results were poor when compared with semi-manual classification. It is known that the NTBC data is highly non-normal and it was suspected that this affected the poor result...

Clustering is an important tool for data mining, since it can identify major patterns or trends without any supervision (labeled data). Over the past five years, semi-supervised (constrained) clustering methods have become very popular. These methods began with incorporating pairwise constraints and have developed into more general methods that can learn appropriate distance metrics. However, s...

We propose an algorithm to effectively cluster a specific type of text documents: textual responses gathered through a survey system. Due to the peculiar features exhibited in such responses (e.g., short in length, rich in outliers, and diverse in categories), traditional unsupervised and semisupervised clustering techniques are challenged to achieve satisfactory performance as demanded by a su...

Developing an effective and impartial grading system for short answers is a challenging problem in educational measurement and assessment, due to the diversity of answers and the subjectivity of graders. In this paper, we design an automatic grading approach for short answers, based on the non-negative semi-supervised document clustering method. After assigning several answer keys, our approach...

The huge amount of currently available data puts considerable constraints on the task of information retrieval. Automatic methods to organize data, such as clustering, can be used to help with this task allowing timely access. Semi-supervised clustering approaches employ some additional information to guide the clustering performed based on data attributes to a more suitable data partition. How...

In this paper we present a procedure to deal with a kind of single-stage decision problems with imprecise utilities. In this type of problems the product measurability of the utility function is not required. So that, the involved expectations are calculated by means of iterated integrals instead of integrals over product spaces. Keywords— Bayesian decision analysis, Iterated expectation, Kudo-...