Frequent subgraph mining from streams of linked graph structured data

Nowadays, high volumes of high-value data (e.g., semantic web data) can be generated and published at a high velocity. A collection of these data can be viewed as a big, interlinked, dynamic graph structure of linked resources. Embedded in them are implicit, previously unknown, and potentially useful knowledge. Hence, ecient knowledge discovery algorithms for mining frequent subgraphs from these dynamic, streaming graph structured data are in demand. Some existing algorithms require very large memory space to discover frequent subgraphs; some others discover collections of frequently co-occurring edges (which may be disjoint). In contrast, we propose|in this paper|algorithms that use limited memory space for discovering collections of frequently co-occurring connected edges. Evaluation results show the effectiveness of our algorithms in frequent subgraph mining from streams of linked graph structured data

Data analytics on the board game Go for the discovery of interesting sequences of moves in joseki

Data analytics on the board game Go for the discovery of interesting sequences of moves in josek

Approximation to expected support of frequent itemsets in mining probabilistic sets of uncertain data

Knowledge discovery and data mining generally discovers implicit, previously unknown, and useful knowledge from data. As one of the popular knowledge discovery and data mining tasks, frequent itemset mining, in particular, discovers knowledge in the form of sets of frequently co-occurring items, events, or objects. On the one hand, in many real-life applications, users mine frequent patterns from traditional databases of precise data, in which users know certainly the presence of items in transactions. On the other hand, in many other real-life applications, users mine frequent itemsets from probabilistic sets of uncertain data, in which users are uncertain about the likelihood of the presence of items in transactions. Each item in these probabilistic sets of uncertain data is often associated with an existential probability expressing the likelihood of its presence in that transaction. To mine frequent itemsets from these probabilistic datasets, many existing algorithms capture lots of information to compute expected support. To reduce the amount of space required, algorithms capture some but not all information in computing or approximating expected support. The tradeoff is that the upper bounds to expected support may not be tight. In this paper, we examine several upper bounds and recommend to the user which ones consume less space while providing good approximation to expected support of frequent itemsets in mining probabilistic sets of uncertain data

Edge-based mining of frequent subgraphs from graph streams

In the current era of Big data, high volumes of valuable data can be generated at a high velocity from high-varieties of data sources in various real-life applications ranging from sensor networks to social networks, from bio-informatics to chemical informatics. In addition, Big data are also available in business, education, engineering, finance, healthcare, scientific, telecommunication, and transportation domains. A collection of these data can be viewed as a big dynamic graph structure. Embedded in them are implicit, previously unknown, and potentially useful knowledge. Consequently, efficient knowledge discovery algorithms for mining frequent subgraphs from these dynamic streaming graph structured data are in demand. On the one hand, some existing algorithms discover collections of frequently co-occurring edges, which may be disjoint. On the other hand, some other existing algorithms discover frequent subgraphs by requiring very large memory space. With high volumes of Big data, available memory space may be limited. To discover collections of frequently co-occurring connected edges, we present in this paper two efficient algorithms that require small memory space. Evaluation results show the efficiency of our edge-based algorithms in mining frequent subgraphs from graph streams

An Effective and Efficient Graph Representation Learning Approach for Big Graphs

In the Big Data era, large graph datasets are becoming increasingly popular due to their capability to integrate and interconnect large sources of data in many fields, e.g., social media, biology, communication networks, etc. Graph representation learning is a flexible tool that automatically extracts features from a graph node. These features can be directly used for machine learning tasks. Graph representation learning approaches producing features preserving the structural information of the graphs are still an open problem, especially in the context of large-scale graphs. In this paper, we propose a new fast and scalable structural representation learning approach called SparseStruct. Our approach uses a sparse internal representation for each node, and we formally proved its ability to preserve structural information. Thanks to a light-weight algorithm where each iteration costs only linear time in the number of the edges, SparseStruct is able to easily process large graphs. In addition, it provides improvements in comparison with state of the art in terms of prediction and classification accuracy by also providing strong robustness to noise data

MINING FREQUENT PATTERNS FROM PRECISE AND UNCERTAIN DATA // MINERAÇÃO DE PADRÕES FREQUENTES A PARTIR DE DADOS PRECISOS E INCERTOS

Data mining has gained popularity over the past two decades and has been considered one of the most prominent areas of current database research. Common data mining tasks include finding frequent patterns, clustering and classifying objects, as well as detecting anomalies. To handle these tasks, techniques from different fields—such as database systems, machine learning, statistics, information retrieval, and data visualization—are applied to provide business intelligent (BI) solutions to various real-life problems. In this survey, we focus on the task of frequent pattern mining, which non-trivially extracts implicit, previously unknown and potentially useful information in the form of frequently occurring sets of items. Mined frequent patterns can be considered as building blocks for association rules, which help reveal associative relationships between items or events on the antecedent and the consequent of rules. Here, we describe some classical algorithms, as well as some recent innovative algorithms, for mining precise data (in which users are certain about the presence or absence of data items) and uncertain data (in which users are uncertain about the presence or absence of data items and they only know that data items probably occur). Mineração de Dados ganhou popularidade nas últimas duas décadas e tem sido considerada uma das mais proeminentes áreas dentro da área de Banco de Dados. Dentre as tarefas comumente realizadas em mineração de dados encontram-se busca de padrões frequentes, clusterização e classificação de objetos, como também detecção de anomalias. Para manipular estas tarefas, técnicas de diferentes campos – tais como sistemas de banco de dados, máquinas de aprendizado, estatística, recuperação de informações e visualização de dados – são aplicadas para oferecer soluções para problemas em nível de Business Intelligent (BI). Nesta pesquisa, nós focamos em tarefas relacionadas a mineração de padrões frequentes, que implica na extração de informações potencialmente úteis, não triviais e previamente desconhecidas, na forma de ocorrências de conjunto de itens frequentes. Mineração de padrões frequentes pode ser considerados como blocos de informações para a construção de regras de associação, os quais auxiliam na identificação de relacionamentos entre itens ou eventos que participam das partes antecedente e consequente de uma regra. Neste trabalho são descritos alguns algoritmos clássicos, como também alguns algoritmos inovadores recentes, para mineração de dados precisos (para os quais o usuário têm certeza da presença ou ausência dos itens de dados) e dados incertos (para os quais usuários tem somente uma certeza probabilística da presença ou ausência de determinados itens de dados)

Item-centric mining of frequent patterns from big uncertain data

Item-centric mining of frequent patterns from big uncertain dat