Classifying complex topics using spatial-semantic document visualization: An evaluation of an interaction model to support open-ended search tasks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In this dissertation we propose, test and develop a novel search interaction model to address two key problems associated with conducting an open-ended search task within a classical information retrieval system: (i) the need to reformulate the query within the context of a shifting conception of the problem and (ii) the need to integrate relevant results across a number of separate results sets. In our model the user issues just one highrecall query and then performs a sequence of more focused, distinct aspect searches by browsing the static structured context of a spatial-semantic visualization of this retrieved document set. Our thesis is that unsupervised spatial-semantic visualization can automatically classify retrieved documents into a two-level hierarchy of relevance. In particular we hypothesise that the locality of any given aspect exemplar will tend to comprise a sufficient proportion of same-aspect documents to support a visually guided strategy for focused, same-aspect searching that we term the aspect cluster growing strategy. We examine spatial-semantic classification and potential aspect cluster growing performance across three scenarios derived from topics and relevance judgements from the TREC test collection. Our analyses show that the expected classification can be represented in spatial-semantic structures created from document similarities computed by a simple vector space text analysis procedure. We compare two diametrically opposed approaches to layout optimisation: a global approach that focuses on preserving the all similarities and a local approach that focuses only on the strongest similarities. We find that the local approach, based on a minimum spanning tree of similarities, produces a better classification and, as observed from strategy simulation, more efficient aspect cluster growing performance in most situations, compared to the global approach of multidimensional scaling. We show that a small but significant proportion of aspect clustering growing cases can be problematic, regardless of the layout algorithm used. We identify the characteristics of these cases and, on this basis, demonstrate a set of novel interactive tools that provide additional semantic cues to aid the user in locating same-aspect documents

From insights to innovations : data mining, visualization, and user interfaces

This thesis is about data mining (DM) and visualization methods for gaining insight into multidimensional data. Novel, exploratory data analysis tools and adaptive user interfaces are developed by tailoring and combining existing DM and visualization methods in order to advance in different applications. The thesis presents new visual data mining (VDM) methods that are also implemented in software toolboxes and applied to industrial and biomedical signals: First, we propose a method that has been applied to investigating industrial process data. The self-organizing map (SOM) is combined with scatterplots using the traditional color linking or interactive brushing. The original contribution is to apply color linked or brushed scatterplots and the SOM to visually survey local dependencies between a pair of attributes in different parts of the SOM. Clusters can be visualized on a SOM with different colors, and we also present how a color coding can be automatically obtained by using a proximity preserving projection of the SOM model vectors. Second, we present a new method for an (interactive) visualization of cluster structures in a SOM. By using a contraction model, the regular grid of a SOM visualization is smoothly changed toward a presentation that shows better the proximities in the data space. Third, we propose a novel VDM method for investigating the reliability of estimates resulting from a stochastic independent component analysis (ICA) algorithm. The method can be extended also to other problems of similar kind. As a benchmarking task, we rank independent components estimated on a biomedical data set recorded from the brain and gain a reasonable result. We also utilize DM and visualization for mobile-awareness and personalization. We explore how to infer information about the usage context from features that are derived from sensory signals. The signals originate from a mobile phone with on-board sensors for ambient physical conditions. In previous studies, the signals are transformed into descriptive (fuzzy or binary) context features. In this thesis, we present how the features can be transformed into higher-level patterns, contexts, by rather simple statistical methods: we propose and test using minimum-variance cost time series segmentation, ICA, and principal component analysis (PCA) for this purpose. Both time-series segmentation and PCA revealed meaningful contexts from the features in a visual data exploration. We also present a novel type of adaptive soft keyboard where the aim is to obtain an ergonomically better, more comfortable keyboard. The method starts from some conventional keypad layout, but it gradually shifts the keys into new positions according to the user's grasp and typing pattern. Related to the applications, we present two algorithms that can be used in a general context: First, we describe a binary mixing model for independent binary sources. The model resembles the ordinary ICA model, but the summation is replaced by the Boolean operator OR and the multiplication by AND. We propose a new, heuristic method for estimating the binary mixing matrix and analyze its performance experimentally. The method works for signals that are sparse enough. We also discuss differences on the results when using different objective functions in the FastICA estimation algorithm. Second, we propose "global iterative replacement" (GIR), a novel, greedy variant of a merge-split segmentation method. Its performance compares favorably to that of the traditional top-down binary split segmentation algorithm.reviewe

3rd Workshop in Symbolic Data Analysis: book of abstracts

This workshop is the third regular meeting of researchers interested in Symbolic Data Analysis. The main aim of the event is to favor the meeting of people and the exchange of ideas from different fields - Mathematics, Statistics, Computer Science, Engineering, Economics, among others - that contribute to Symbolic Data Analysis

Dissimilarity-based learning for complex data

Mokbel B. Dissimilarity-based learning for complex data. Bielefeld: Universität Bielefeld; 2016.Rapid advances of information technology have entailed an ever increasing amount of digital data, which raises the demand for powerful data mining and machine learning tools. Due to modern methods for gathering, preprocessing, and storing information, the collected data become more and more complex: a simple vectorial representation, and comparison in terms of the Euclidean distance is often no longer appropriate to capture relevant aspects in the data. Instead, problem-adapted similarity or dissimilarity measures refer directly to the given encoding scheme, allowing to treat information constituents in a relational manner. This thesis addresses several challenges of complex data sets and their representation in the context of machine learning. The goal is to investigate possible remedies, and propose corresponding improvements of established methods, accompanied by examples from various application domains. The main scientific contributions are the following: (I) Many well-established machine learning techniques are restricted to vectorial input data only. Therefore, we propose the extension of two popular prototype-based clustering and classification algorithms to non-negative symmetric dissimilarity matrices. (II) Some dissimilarity measures incorporate a fine-grained parameterization, which allows to configure the comparison scheme with respect to the given data and the problem at hand. However, finding adequate parameters can be hard or even impossible for human users, due to the intricate effects of parameter changes and the lack of detailed prior knowledge. Therefore, we propose to integrate a metric learning scheme into a dissimilarity-based classifier, which can automatically adapt the parameters of a sequence alignment measure according to the given classification task. (III) A valuable instrument to make complex data sets accessible are dimensionality reduction techniques, which can provide an approximate low-dimensional embedding of the given data set, and, as a special case, a planar map to visualize the data's neighborhood structure. To assess the reliability of such an embedding, we propose the extension of a well-known quality measure to enable a fine-grained, tractable quantitative analysis, which can be integrated into a visualization. This tool can also help to compare different dissimilarity measures (and parameter settings), if ground truth is not available. (IV) All techniques are demonstrated on real-world examples from a variety of application domains, including bioinformatics, motion capturing, music, and education

Statistic Software for Neighbor Embedding

Dimension reduction presents expanding importance and prevalence since it lessens the challenge to data visualization and exploratory analysis that numerous science areas rely on. Recently, nonlinear dimension reduction (NLDR) methods have achieved superior performance in coping with complicated data manifolds embedded in high dimensional space. However, conventional statistic software for NLDR visualization purpose (e.g Multidimensional Scaling) often gives undesired desirable layouts. In this thesis work, to improve the performance of NLDR for data visualization, we study the recently proposed and efficient neighbor embedding (NE) framework and develop its software package in statistic software R. The neighbor embedding framework consists of a wide family of NLDR including stochastic neighbor embedding (SNE), symmetric SNE etc. Yet the original SNE optimization algorithm has several drawbacks. For example, it cannot be extended to other NE objective functions and requires quadratic computation cost. To address these drawbacks, we unify many different NE objective functions through several software layers and adopt a tree-based approach for computation acceleration. The core algorithm is implemented in C++ with an lightweight R wrapper. It thus provides an efficient and convenient package for researchers and engineers who work on statistics. We demonstrate the developed software by visualizing the two-dimensional layouts for several typical datasets in machine learning research including MNIST, COIL-20 and Phonemes etc. The results show that NE methods significantly outperform the traditional MDS visualization tool, indicating NE as a promising and useful dimension reduction tool for data visualization in statistics