Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis

In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.

Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis

Small World Folksonomies: Clustering in Tri-Partite Hypergraphs

Many recent Web 2.0 resource sharing applications can be subsumed under the "folksonomy" moniker. Regardless of the type of resource shared, all of these share a common structure describing the assignment of tags to resources by users. In this report, we generalize the notions of clustering and characteristic path length which play a major role in the current research on networks, where they are used to describe the small-world effects on many observable network datasets. To that end, we show that the notion of clustering has two facets which are not equivalent in the generalized setting. The new measures are evaluated on two large-scale folksonomy datasets from resource sharing systems on the web.

Protein-Expressions-Analysen zur Etablierung prädiktiver Biomarker bei der Behandlung fortgeschrittener Tumoren der Mamma und der Prostata

Aktuelle Entwicklungen auf dem Gebiet der zielgerichteten Therapie zur Behandlung maligner Erkrankungen erfordern neuartige Verfahren zur Diagnostik und Selektion geeigneter Patienten. So ist das Ziel der vorliegenden Arbeit die Identifizierung neuer Zielmoleküle, die die Vorhersage eines Therapieerfolges mit targeted drugs ermöglichen. Besondere Aufmerksamkeit gilt dem humanisierten monoklonalen Antikörper Trastuzumab (Herceptin), der zur Therapie Her-2 überexprimierender, metastasierter Mammakarzinome eingesetzt wird. Jüngste Erkenntnisse lassen eine Anwendung dieses Medikamentes in der Behandlung des Hormon-unabhängigen Prostatakarzinoms möglich erscheinen. Therapie-beeinflussende Faktoren werden in der dem Rezeptor nachgeschalteten Signaltransduktion oder Veränderungen des Rezeptors selbst vermutet. Mittels Immunhistochemie wurden die Expressions- und Aktivierungsniveaus verschiedener Proteine der Her-2-assoziierten Signaltransduktion ermittelt; insgesamt wurden 37 molekulare Marker untersucht. In Formalin fixierte und in Paraffin eingebettete korrespondierende Normal- und Tumorgewebe von 118 Mammakarzinom-Patientinnen sowie 78 Patienten mit Prostatakarzinom wurden in TMAs zusammengefasst. Die in Zusammenarbeit mit erfahrenen Pathologen ermittelten Ergebnisse dienten u.a. als Grundlage für zweidimensionales, unsupervised hierarchisches clustering. Ergebnis dieser Analysen war für beide untersuchten Tumorentitäten die Möglichkeit einer Subklassifizierung der untersuchten Populationen nach molekularen Eigenschaften. Hierbei zeigten sich jeweils neue Möglichkeiten zur Anwendung zielgerichteter Therapien, deren Effektivität Inhalt weiterführender Studien sein könnte. Zusätzlich wurden an insgesamt 43 Frischgeweben die möglichen Folgen des sog. shedding untersucht. Western Blot-basierte Untersuchungen zeigten hierbei die Möglichkeit der Selektion von Patienten aufgrund falsch-positiver Befunde in der derzeit als Standard geltenden Diagnostik. Zusätzlich konnte durch Vergleich mit einer Herceptin-sensitiven Zelllinie ein möglicher Zusammenhang eines Therapieerfolges mit dem Phosphorylierungs-/ Aktivierungszustand des Rezeptors ermittelt werden. Fehlende klinische Daten zum Verlauf der Erkrankung und Therapie der untersuchten Patienten lassen keine Aussagen über die tatsächliche Relevanz der ermittelten Befunde zu. Dennoch verdeutlichen die erhaltenen Resultate eindrucksvoll die Komplexität der molekularen Vorgänge, die zu einem Krebsgeschehen führen und damit Auswirkungen auf die Wirksamkeit von targeted drugs haben können. Entwicklungen auf dem Gebiet der zielgerichteten Therapie erfordern Verbesserungen auf dem Gebiet der Diagnostik, die die sichere Selektion geeigneter Patienten erlauben. Die Zukunft der personalisierten, zielgerichteten Behandlung von Tumorerkrankungen wird verstärkt von molekularen Markerprofilen hnlich den hier vorgestellten Daten beeinflusst werden.

Efficient data mining based on formal concept analysis

Formal Concept Analysis is an unsupervised learning technique for conceptual clustering. We introduce the notion of iceberg concept lattices and show their use in Knowledge Discovery in Databases (KDD). Iceberg lattices are designed for analyzing very large databases. In particular they serve as a condensed representation of frequent patterns as known from association rule mining. In order to show the interplay between Formal Concept Analysis and association rule mining, we discuss the algorithm TITANIC. We show that iceberg concept lattices are a starting point for computing condensed sets of association rules without loss of information, and are a visualization method for the resulting rules.

Content aggregation on knowledge bases using graph clustering

Recently, research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which are targeted at establishing peer-to-peer knowledge management (P2PKM) systems. In such a system, it is necessary to obtain provide brief semantic descriptions of peers, so that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peers a given query should be forwarded. This paper proposes the use of graph clustering techniques on knowledge bases for that purpose. Using this clustering, we can show that our strategy requires up to 58% fewer queries than the baselines to yield full recall in a bibliographic P2PKM scenario.

Optimal Unsupervised Learning in Feedforward Neural Networks

We investigate the properties of feedforward neural networks trained with Hebbian learning algorithms. A new unsupervised algorithm is proposed which produces statistically uncorrelated outputs. The algorithm causes the weights of the network to converge to the eigenvectors of the input correlation with largest eigenvalues. The algorithm is closely related to the technique of Self-supervised Backpropagation, as well as other algorithms for unsupervised learning. Applications of the algorithm to texture processing, image coding, and stereo depth edge detection are given. We show that the algorithm can lead to the development of filters qualitatively similar to those found in primate visual cortex.

The Unsupervised Acquisition of a Lexicon from Continuous Speech

We present an unsupervised learning algorithm that acquires a natural-language lexicon from raw speech. The algorithm is based on the optimal encoding of symbol sequences in an MDL framework, and uses a hierarchical representation of language that overcomes many of the problems that have stymied previous grammar-induction procedures. The forward mapping from symbol sequences to the speech stream is modeled using features based on articulatory gestures. We present results on the acquisition of lexicons and language models from raw speech, text, and phonetic transcripts, and demonstrate that our algorithm compares very favorably to other reported results with respect to segmentation performance and statistical efficiency.

Clustering compositional data trajectories

Our essay aims at studying suitable statistical methods for the clustering of compositional data in situations where observations are constituted by trajectories of compositional data, that is, by sequences of composition measurements along a domain. Observed trajectories are known as “functional data” and several methods have been proposed for their analysis. In particular, methods for clustering functional data, known as Functional Cluster Analysis (FCA), have been applied by practitioners and scientists in many fields. To our knowledge, FCA techniques have not been extended to cope with the problem of clustering compositional data trajectories. In order to extend FCA techniques to the analysis of compositional data, FCA clustering techniques have to be adapted by using a suitable compositional algebra. The present work centres on the following question: given a sample of compositional data trajectories, how can we formulate a segmentation procedure giving homogeneous classes? To address this problem we follow the steps described below. First of all we adapt the well-known spline smoothing techniques in order to cope with the smoothing of compositional data trajectories. In fact, an observed curve can be thought of as the sum of a smooth part plus some noise due to measurement errors. Spline smoothing techniques are used to isolate the smooth part of the trajectory: clustering algorithms are then applied to these smooth curves. The second step consists in building suitable metrics for measuring the dissimilarity between trajectories: we propose a metric that accounts for difference in both shape and level, and a metric accounting for differences in shape only. A simulation study is performed in order to evaluate the proposed methodologies, using both hierarchical and partitional clustering algorithm. The quality of the obtained results is assessed by means of several indices

Integració a la plataforma webDTI de tècniques de clustering de fibres i visualització de clústers

Estudi, disseny i implementació de diferents tècniques d’agrupament de fibres (clustering) per tal d’integrar a la plataforma DTIWeb diferents algorismes de clustering i tècniques de visualització de clústers de fibres de forma que faciliti la interpretació de dades de DTI als especialistes

A new approach to segmentation based on fusing circumscribed contours, region growing and clustering

One of the major problems in machine vision is the segmentation of images of natural scenes. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. The main contours of the scene are detected and used to guide the posterior region growing process. The algorithm places a number of seeds at both sides of a contour allowing stating a set of concurrent growing processes. A previous analysis of the seeds permits to adjust the homogeneity criterion to the regions's characteristics. A new homogeneity criterion based on clustering analysis and convex hull construction is proposed

Unsupervised active regions for multiresolution image segmentation

An unsupervised approach to image segmentation which fuses region and boundary information is presented. The proposed approach takes advantage of the combined use of 3 different strategies: the guidance of seed placement, the control of decision criterion, and the boundary refinement. The new algorithm uses the boundary information to initialize a set of active regions which compete for the pixels in order to segment the whole image. The method is implemented on a multiresolution representation which ensures noise robustness as well as computation efficiency. The accuracy of the segmentation results has been proven through an objective comparative evaluation of the method

Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach

Modelo de segmentación : estudio, aplicación, ejecución y desarrollo de estrategias para la implementación de un modelo de clustering en el Sector Financiero

A finales de 2009 se emprendió un nuevo modelo de segmentación de mercados por conglomeraciones o clústers, con el cual se busca atender las necesidades de los clientes, advirtiendo el ciclo de vida en el cual se encuentran, realizando estrategias que mejoren la rentabilidad del negocio, por medio de indicadores de gestión KPI. Por medio de análisis tecnológico se desarrolló el proceso de inteligencia de la segmentación, por medio del cual se obtuvo el resultado de clústers, que poseían características similares entre sí, pero que diferían de los otros, en variables de comportamiento. Esto se refleja en el desarrollo de campañas estratégicas dirigidas que permitan crear una estrecha relación de fidelidad con el cliente, para aumentar la rentabilidad, en principio, y fortalecer la relación a largo plazo, respondiendo a la razón de ser del negocio

A Homogeneous Set-Theoretical Frame for Clustering Fuzzy Relational Data

Our purpose is to provide a set-theoretical frame to clustering fuzzy relational data basically based on cardinality of the fuzzy subsets that represent objects and their complementaries, without applying any crisp property. From this perspective we define a family of fuzzy similarity indexes which includes a set of fuzzy indexes introduced by Tolias et al, and we analyze under which conditions it is defined a fuzzy proximity relation. Following an original idea due to S. Miyamoto we evaluate the similarity between objects and features by means the same mathematical procedure. Joining these concepts and methods we establish an algorithm to clustering fuzzy relational data. Finally, we present an example to make clear all the process