Semi-supervised heterogeneous evolutionary co-clustering

One of the challenges of the machine learning problem is the absence of sufficient number of labeled instances or training instances. At the same time generating labeled data is expensive and time consuming. The semi-supervised approach has shown promising results to solve the problem of insufficient or fewer labeled instance datasets. The key challenge is incorporating the semi-supervised knowledge into the heterogeneous data which is evolving in nature. Most of the prior work that uses semi-supervised knowledge has been performed on heterogeneous static data. The semi-supervised knowledge is incorporated into data which aid the clustering algorithm to obtain better clusters. The semi-supervised knowledge is provided as constrained based or distance based. I am proposing a framework to incorporate prior knowledge to perform co-clustering on the evolving heterogeneous data. This framework can be used to solve a wide range of problems dealing with text analysis, web analysis and image grouping. In the semi-supervised approach we incorporate the domain knowledge by placing the constraints which aid the clustering process in performing effective clustering of the data. In the proposed framework, I am using the constraint based semi-supervised non-negative matrix factorization approach to obtain the co-clustering on the heterogeneous evolving data. The constraint based semi-supervised approach uses the user provided must-link or cannot-link constraints on the central data type before performing co-clustering. To process the original datasets efficiently in terms of time and space I am using the low rank approximation technique to obtain the sparse representation of the input data matrix using the Dynamic Colibri approach

Business Process Modeling Abstraction Based on Semi-Supervised Clustering Analysis

The most prominent Business Process Model Abstraction (BPMA) use case is the construction of the process “quick view” for rapidly comprehending a complex process. Some researchers propose process abstraction methods to aggregate the activities on the basis of their semantic similarity. One important clustering technique used in these methods is traditional k-means cluster analysis which so far is an unsupervised process without any priori information, and most of the techniques aggregate the activities only according to business semantics without considering the requirement of an order-preserving model transformation. The paper proposes a BPMA method based on semi-supervised clustering which chooses the initial clusters based on the refined process structure tree and designs constraints by combining the control flow consistency of the process and the semantic similarity of the activities to guide the clustering process. To be more precise, the constraint function is discovered by mining from a process model collection enriched with subprocess relations. The proposed method is validated by applying it to a process model repository in use. In an experimental validation, the proposed method is compared to the traditional k-means clustering (parameterized with randomly chosen initial clusters and an only semantics-based distance measure), showing that the approach closely approximates the decisions of the involved modelers to cluster activities. As such, the paper contributes to the development of modeling support for effective process model abstraction, facilitating the use of business process models in practice

Role based behavior analysis

Tese de mestrado, Segurança Informática, Universidade de Lisboa, Faculdade de Ciências, 2009Nos nossos dias, o sucesso de uma empresa depende da sua agilidade e capacidade de se adaptar a condições que se alteram rapidamente. Dois requisitos para esse sucesso são trabalhadores proactivos e uma infra-estrutura ágil de Tecnologias de Informacão/Sistemas de Informação (TI/SI) que os consiga suportar. No entanto, isto nem sempre sucede. Os requisitos dos utilizadores ao nível da rede podem nao ser completamente conhecidos, o que causa atrasos nas mudanças de local e reorganizações. Além disso, se não houver um conhecimento preciso dos requisitos, a infraestrutura de TI/SI poderá ser utilizada de forma ineficiente, com excessos em algumas áreas e deficiências noutras. Finalmente, incentivar a proactividade não implica acesso completo e sem restrições, uma vez que pode deixar os sistemas vulneráveis a ameaças externas e internas. O objectivo do trabalho descrito nesta tese é desenvolver um sistema que consiga caracterizar o comportamento dos utilizadores do ponto de vista da rede. Propomos uma arquitectura de sistema modular para extrair informação de fluxos de rede etiquetados. O processo é iniciado com a criação de perfis de utilizador a partir da sua informação de fluxos de rede. Depois, perfis com características semelhantes são agrupados automaticamente, originando perfis de grupo. Finalmente, os perfis individuais são comprados com os perfis de grupo, e os que diferem significativamente são marcados como anomalias para análise detalhada posterior. Considerando esta arquitectura, propomos um modelo para descrever o comportamento de rede dos utilizadores e dos grupos. Propomos ainda métodos de visualização que permitem inspeccionar rapidamente toda a informação contida no modelo. O sistema e modelo foram avaliados utilizando um conjunto de dados reais obtidos de um operador de telecomunicações. Os resultados confirmam que os grupos projectam com precisão comportamento semelhante. Além disso, as anomalias foram as esperadas, considerando a população subjacente. Com a informação que este sistema consegue extrair dos dados em bruto, as necessidades de rede dos utilizadores podem sem supridas mais eficazmente, os utilizadores suspeitos são assinalados para posterior análise, conferindo uma vantagem competitiva a qualquer empresa que use este sistema.In our days, the success of a corporation hinges on its agility and ability to adapt to fast changing conditions. Proactive workers and an agile IT/IS infrastructure that can support them is a requirement for this success. Unfortunately, this is not always the case. The user’s network requirements may not be fully understood, which slows down relocation and reorganization. Also, if there is no grasp on the real requirements, the IT/IS infrastructure may not be efficiently used, with waste in some areas and deficiencies in others. Finally, enabling proactivity does not mean full unrestricted access, since this may leave the systems vulnerable to outsider and insider threats. The purpose of the work described on this thesis is to develop a system that can characterize user network behavior. We propose a modular system architecture to extract information from tagged network flows. The system process begins by creating user profiles from their network flows’ information. Then, similar profiles are automatically grouped into clusters, creating role profiles. Finally, the individual profiles are compared against the roles, and the ones that differ significantly are flagged as anomalies for further inspection. Considering this architecture, we propose a model to describe user and role network behavior. We also propose visualization methods to quickly inspect all the information contained in the model. The system and model were evaluated using a real dataset from a large telecommunications operator. The results confirm that the roles accurately map similar behavior. The anomaly results were also expected, considering the underlying population. With the knowledge that the system can extract from the raw data, the users network needs can be better fulfilled, the anomalous users flagged for inspection, giving an edge in agility for any company that uses it

Discrete Signal Processing on Graphs: Frequency Analysis

Signals and datasets that arise in physical and engineering applications, as well as social, genetics, biomolecular, and many other domains, are becoming increasingly larger and more complex. In contrast to traditional time and image signals, data in these domains are supported by arbitrary graphs. Signal processing on graphs extends concepts and techniques from traditional signal processing to data indexed by generic graphs. This paper studies the concepts of low and high frequencies on graphs, and low-, high-, and band-pass graph filters. In traditional signal processing, there concepts are easily defined because of a natural frequency ordering that has a physical interpretation. For signals residing on graphs, in general, there is no obvious frequency ordering. We propose a definition of total variation for graph signals that naturally leads to a frequency ordering on graphs and defines low-, high-, and band-pass graph signals and filters. We study the design of graph filters with specified frequency response, and illustrate our approach with applications to sensor malfunction detection and data classification

From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field