Labeling document clusters using word embeddings

Dokumente lahko na različne načine predstavimo z vektorji ter jih vizualiziramo v dvorazsežnem prostoru. V tem prostoru lahko poiščemo skupine podobnih dokumentov in nato poiščemo besede, ki dobro opisujejo posamezne skupine. Vizualizacijo dokumentov lahko obogatimo s prikazom najdenih besed. Za to se uporabljajo metode za označevanje skupin dokumentov, ki temeljijo na uporabi mer pomembnosti, ki upoštevajo le frekvence besed v danem korpusu. V tem diplomskem delu predlagamo novo metodo za označevanje skupin dokumentov, ki za vložitev dokumentov in besed uporablja prednaučene modele za vložitev besed ter temelji na predpostavki, da so podobne besede predstavljene s podobnimi vektorji. Modele za vložitev besed med sabo primerjamo s stališča medsebojne podobnosti in uspešnosti na klasifikacijskih nalogah, da bi izbrali tistega, ki ga bomo uporabili v kombinaciji z metodo za označevanje skupin dokumentov. Metodo empirično ovrednotimo ter jo primerjamo z že obstoječim pristopom in pokažemo, da zaradi uporabe prednaučenih modelov lahko uspešno dela tudi na zelo majhnih podatkovnih množicah, česar že obstoječi pristop ne zmore.Documents can be represented as vectors in various ways and visualized in two-dimensional space. In that space, we can find clusters of similar documents and the words that describe each cluster as well as possible. Those words can be added to the visualization to enrich it. This can be achieved by using methods for labeling document clusters. These methods use the frequencies of words in a given corpus to measure the importance of each word. In this thesis we propose a novel method for labeling clusters of documents. The method is based on using pre-trained word embedding models to embed both words and documents and utilizes the assumption that the similar words are represented with similar vectors. We compare word embedding models by computing their similarities and scores achieved on classification tasks to choose the one to use in combination with our method. Method is empirically evaluated and compared with the traditional approach. We show that compared to the traditional approach, our method can work on very small datasets due to the fact that it uses the pre-trained models to obtain the embeddings

Iterative Visual Analytics and its Applications in Bioinformatics

Indiana University-Purdue University Indianapolis (IUPUI)You, Qian. Ph.D., Purdue University, December, 2010. Iterative Visual Analytics and its Applications in Bioinformatics. Major Professors: Shiaofen Fang and Luo Si. Visual Analytics is a new and developing field that addresses the challenges of knowledge discoveries from the massive amount of available data. It facilitates humans‘ reasoning capabilities with interactive visual interfaces for exploratory data analysis tasks, where automatic data mining methods fall short due to the lack of the pre-defined objective functions. Analyzing the large volume of data sets for biological discoveries raises similar challenges. The domain knowledge of biologists and bioinformaticians is critical in the hypothesis-driven discovery tasks. Yet developing visual analytics frameworks for bioinformatic applications is still in its infancy. In this dissertation, we propose a general visual analytics framework – Iterative Visual Analytics (IVA) – to address some of the challenges in the current research. The framework consists of three progressive steps to explore data sets with the increased complexity: Terrain Surface Multi-dimensional Data Visualization, a new multi-dimensional technique that highlights the global patterns from the profile of a large scale network. It can lead users‘ attention to characteristic regions for discovering otherwise hidden knowledge; Correlative Multi-level Terrain Surface Visualization, a new visual platform that provides the overview and boosts the major signals of the numeric correlations among nodes in interconnected networks of different contexts. It enables users to gain critical insights and perform data analytical tasks in the context of multiple correlated networks; and the Iterative Visual Refinement Model, an innovative process that treats users‘ perceptions as the objective functions, and guides the users to form the optimal hypothesis by improving the desired visual patterns. It is a formalized model for interactive explorations to converge to optimal solutions. We also showcase our approach with bio-molecular data sets and demonstrate its effectiveness in several biomarker discovery applications

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions