Perceptually relevant browsing environments for large texture databases

This thesis describes the development of a large database of texture stimuli, the production of a similarity matrix re ecting human judgements of similarity about the database, and the development of three browsing models that exploit structure in the perceptual information for navigation. Rigorous psychophysical comparison experiments are carried out and the SOM (Self Organising Map) found to be the fastest of the three browsing models under examination. We investigate scalable methods of augmenting a similarity matrix using the SOM browsing environment to introduce previously unknown textures. Further psychophysical experiments reveal our method produces a data organisation that is as fast to navigate as that derived from the perceptual grouping experiments.Engineering and Physical Sciences Research Council (EPSRC

Automatic Labelling and Document Clustering for Forensic Analysis

In computer forensic analysis, retrieved data is in unstructured text, whose analysis by computer examiners is difficult to be performed. In proposed approach the forensic analysis is done very systematically i.e. retrieved data is in unstructured format get particular structure by using high quality well known algorithm and automatic cluster labelling method. Indexing is performed on txt, doc, and pdf file which automatically estimate the number of clusters with automatic labelling to it. In the proposed approach DBSCAN algorithm and K-mean algorithm are used; which makes it very easy to retrieve most relevant information for forensic analysis also the automated methods of analysis are of great interest. In particular, algorithms for clustering documents can facilitate the discovery of new and useful knowledge from the documents under analysis. Two methods are used for document clustering for forensic analysis; the first method uses an x2 test of significance to detect different word usage across categories in the hierarchy which is well suited for testing dependencies when count data is available. The second method selects words which both occur frequently in a cluster and effectively discriminate the given cluster from the other clusters. Finally, we also present and discuss several practical results that can be useful for researchers of forensic analysis

Addressing the new generation of spam (Spam 2.0) through Web usage models

New Internet collaborative media introduce new ways of communicating that are not immune to abuse. A fake eye-catching profile in social networking websites, a promotional review, a response to a thread in online forums with unsolicited content or a manipulated Wiki page, are examples of new the generation of spam on the web, referred to as Web 2.0 Spam or Spam 2.0. Spam 2.0 is defined as the propagation of unsolicited, anonymous, mass content to infiltrate legitimate Web 2.0 applications.The current literature does not address Spam 2.0 in depth and the outcome of efforts to date are inadequate. The aim of this research is to formalise a definition for Spam 2.0 and provide Spam 2.0 filtering solutions. Early-detection, extendibility, robustness and adaptability are key factors in the design of the proposed method.This dissertation provides a comprehensive survey of the state-of-the-art web spam and Spam 2.0 filtering methods to highlight the unresolved issues and open problems, while at the same time effectively capturing the knowledge in the domain of spam filtering.This dissertation proposes three solutions in the area of Spam 2.0 filtering including: (1) characterising and profiling Spam 2.0, (2) Early-Detection based Spam 2.0 Filtering (EDSF) approach, and (3) On-the-Fly Spam 2.0 Filtering (OFSF) approach. All the proposed solutions are tested against real-world datasets and their performance is compared with that of existing Spam 2.0 filtering methods.This work has coined the term ‘Spam 2.0’, provided insight into the nature of Spam 2.0, and proposed filtering mechanisms to address this new and rapidly evolving problem

The Role of String Similarity Metrics in Ontology Alignment

Tim Berners-Lee originally envisioned a much different world wide web than the one we have today - one that computers as well as humans could search for the information they need [3]. There are currently a wide variety of research efforts towards achieving this goal, one of which is ontology alignment

Cyberspace and Real-World Behavioral Relationships: Towards the Application of Internet Search Queries to Identify Individuals At-risk for Suicide

The Internet has become an integral and pervasive aspect of society. Not surprisingly, the growth of ecommerce has led to focused research on identifying relationships between user behavior in cyberspace and the real world - retailers are tracking items customers are viewing and purchasing in order to recommend additional products and to better direct advertising. As the relationship between online search patterns and real-world behavior becomes more understood, the practice is likely to expand to other applications. Indeed, Google Flu Trends has implemented an algorithm that accurately charts the relationship between the number of people searching for flu-related topics on the Internet, and the number of people who actually have flu symptoms in that region. Because the results are real-time, studies show Google Flu Trends estimates are typically two weeks ahead of the Center for Disease Control. The Air Force has devoted considerable resources to suicide awareness and prevention. Despite these efforts, suicide rates have remained largely unaffected. The Air Force Suicide Prevention Program assists family, friends, and co-workers of airmen in recognizing and discussing behavioral changes with at-risk individuals. Based on other successes in correlating behaviors in cyberspace and the real world, is it possible to leverage online activities to help identify individuals that exhibit suicidal or depression-related symptoms? This research explores the notion of using Internet search queries to classify individuals with common search patterns. Text mining was performed on user search histories for a one-month period from nine Air Force installations. The search histories were clustered based on search term probabilities, providing the ability to identify relationships between individuals searching for common terms. Analysis was then performed to identify relationships between individuals searching for key terms associated with suicide, anxiety, and post-traumatic stress

Crowdsourced intuitive visual design feedback

For many people images are a medium preferable to text and yet, with the exception of star ratings, most formats for conventional computer mediated feedback focus on text. This thesis develops a new method of crowd feedback for designers based on images. Visual summaries are generated from a crowd’s feedback images chosen in response to a design. The summaries provide the designer with impressionistic and inspiring visual feedback. The thesis sets out the motivation for this new method, describes the development of perceptually organised image sets and a summarisation algorithm to implement it. Evaluation studies are reported which, through a mixed methods approach, provide evidence of the validity and potential of the new image-based feedback method. It is concluded that the visual feedback method would be more appealing than text for that section of the population who may be of a visual cognitive style. Indeed the evaluation studies are evidence that such users believe images are as good as text when communicating their emotional reaction about a design. Designer participants reported being inspired by the visual feedback where, comparably, they were not inspired by text. They also reported that the feedback can represent the perceived mood in their designs, and that they would be enthusiastic users of a service offering this new form of visual design feedback

Analytical customer relationship management in retailing supported by data mining techniques

Tese de doutoramento. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

Data Mining Using the Crossing Minimization Paradigm

Our ability and capacity to generate, record and store multi-dimensional, apparently unstructured data is increasing rapidly, while the cost of data storage is going down. The data recorded is not perfect, as noise gets introduced in it from different sources. Some of the basic forms of noise are incorrect recording of values and missing values. The formal study of discovering useful hidden information in the data is called Data Mining. Because of the size, and complexity of the problem, practical data mining problems are best attempted using automatic means. Data Mining can be categorized into two types i.e. supervised learning or classification and unsupervised learning or clustering. Clustering only the records in a database (or data matrix) gives a global view of the data and is called one-way clustering. For a detailed analysis or a local view, biclustering or co-clustering or two-way clustering is required involving the simultaneous clustering of the records and the attributes. In this dissertation, a novel fast and white noise tolerant data mining solution is proposed based on the Crossing Minimization (CM) paradigm; the solution works for one-way as well as two-way clustering for discovering overlapping biclusters. For decades the CM paradigm has traditionally been used for graph drawing and VLSI (Very Large Scale Integration) circuit design for reducing wire length and congestion. The utility of the proposed technique is demonstrated by comparing it with other biclustering techniques using simulated noisy, as well as real data from Agriculture, Biology and other domains. Two other interesting and hard problems also addressed in this dissertation are (i) the Minimum Attribute Subset Selection (MASS) problem and (ii) Bandwidth Minimization (BWM) problem of sparse matrices. The proposed CM technique is demonstrated to provide very convincing results while attempting to solve the said problems using real public domain data. Pakistan is the fourth largest supplier of cotton in the world. An apparent anomaly has been observed during 1989-97 between cotton yield and pesticide consumption in Pakistan showing unexpected periods of negative correlation. By applying the indigenous CM technique for one-way clustering to real Agro-Met data (2001-2002), a possible explanation of the anomaly has been presented in this thesis