Quality and interestingness of association rules derived from data mining of relational and semi-structured data

Deriving useful and interesting rules from a data mining system are essential and important tasks. Problems such as the discovery of random and coincidental patterns or patterns with no significant values, and the generation of a large volume of rules from a database commonly occur. Works on sustaining the interestingness of rules generated by data mining algorithms are actively and constantly being examined and developed. As the data mining techniques are data-driven, it is beneficial to affirm the rules using a statistical approach. It is important to establish the ways in which the existing statistical measures and constraint parameters can be effectively utilized and the sequence of their usage.In this thesis, a systematic way to evaluate the association rules discovered from frequent, closed and maximal itemset mining algorithms; and frequent subtree mining algorithm including the rules based on induced, embedded and disconnected subtrees is presented. With reference to the frequent subtree mining, in addition a new direction is explored based on utilizing the DSM approach capable of preserving all information from tree-structured database in a flat data format, consequently enabling the direct application of a wider range of data mining analysis/techniques to tree-structured data. Implications of this approach were investigated and it was found that basing rules on disconnected subtrees, can be useful in terms of increasing the accuracy and the coverage rate of the rule set.A strategy that combines data mining and statistical measurement techniques such as sampling, redundancy and contradictive checks, correlation and regression analysis to evaluate the rules is developed. This framework is then applied to real-world datasets that represent diverse characteristics of data/items. Empirical results show that with a proper combination of data mining and statistical analysis, the proposed framework is capable of eliminating a large number of non-significant, redundant and contradictive rules while preserving relatively valuable high accuracy rules. Moreover, the results reveal the important characteristics and differences between mining frequent, closed or maximal itemsets; and mining frequent subtree including the rules based on induced, embedded and disconnected subtrees; as well as the impact of confidence measure for the prediction and classification task

Tree model guided (TMG) enumeration as the basis for mining frequent patterns from XML documents

University of Technology, Sydney. Faculty of Information Technology.Association mining consists of two important problems, namely frequent patterns discovery and rule construction. The former task is considered to be a more challenging problem to solve. Because of its importance and application in a number of data mining tasks, it has become the focus of many studies. A substantial amount of research has gone into the development of efficient algorithms for mining patterns from large structured or relational data. Compared with the fruitful achievements in mining structured data, mining in the semi-structured world still remains at a preliminary stage. The most popular representative of the semi-structured data is XML. Mining frequent patterns from XML poses more challenges in comparison to mining frequent patterns from relational data because XML is a tree-structured data and has an ordered data context. Moreover, XML data in general is larger in data size due to richer contents and more meta-data. Dealing with XML, thus involves greater unprecedented complexity in comparison to mining relational data. Mining frequent patterns from XML can be recast as mining frequent tree structures from a database of XML documents. The increase of XML data and the need for mining semi-structured data has sparked a lot of interest in finding frequent rooted trees in forests. In this thesis, we aim to develop a framework to mine frequent patterns from XML documents. The framework utilizes a structure-guided enumeration approach, Tree Model Guided (TMG), for efficient enumeration of tree structure and it makes use of novel structures for fast enumeration and frequency counting. By utilizing a novel array-based structure, an embedded list (EL), the framework offers a simple sequencelike tree enumeration technique. The effectiveness and extendibility of the framework is demonstrated in that it can be utilized not only for enumerating ordered subtrees but also for enumerating unordered subtrees and subsequences. Furthermore, the framework tackles the unprecedented complexity in mining frequent tree-structured patterns by generating only valid candidates with non-zero frequency count and employing a constraint-driven approach. Our experimental studies comparing the proposed framework with the state-of-the-art algorithms demonstrate the effectiveness and the efficiency of the proposed framework

Tree model guided candidate generation for mining frequent subtrees from XML

Due to the inherent flexibilities in both structure and semantics, XML association rules mining faces few challenges, such as: a more complicated hierarchical data structure and ordered data context. Mining frequent patterns from XML documents can be recast as mining frequent tree structures from a database of XML documents. In this study, we model a database of XML documents as a database of rooted labeled ordered subtrees. In particular, we are mainly coneerned with mining frequent induced and embedded ordered subtrees. Our main contributions arc as follows. We describe our unique embedding list representation of the tree structure, which enables efficient implementation ofour Tree Model Guided (TMG) candidate generation. TMG is an optimal, non-redundant enumeration strategy which enumerates all the valid candidates that conform to the structural aspects of the data. We show through a mathematical model and experiments that TMG has better complexity compared to the commonly used join approach. In this paper, we propose two algorithms, MB3Miner and iMB3-Miner. MB3-Miner mines embedded subtrees. iMB3-Miner mines induced and/or embedded subtrees by using the maximum level of embedding constraint. Our experiments with both synthetic and real datasets against two well known algorithms for mining induced and embedded subtrees, demonstrate the effeetiveness and the efficiency of the proposed techniques

Razor: Mining distance-constrained embedded subtrees

Our work is focused on the task of mining frequent subtrees from a database of rooted ordered labelled subtrees. Previously we have developed an efficient algorithm, MB3 [12], for mining frequent embedded subtrees from a database of rooted labeled and ordered subtrees. The efficiency comes from the utilization of a novel Embedding List representation for Tree Model Guided (TMG) candidate generation. As an extension the IMB3 [13] algorithm introduces the Level of Embedding constraint. In this study we extend our past work by developing an algorithm, Razor, for mining embedded subtrees where the distance of nodes relative to the root of the subtree needs to be considered. This notion of distance constrained embedded tree mining will have important applications in web information systems, conceptual model analysis and more sophisticated ontology matching. Domains representing their knowledge in a tree structured form may require this additional distance information as it commonly indicates the amount of specific knowledge stored about a particular concept within the hierarchy. The structure based approaches for schema matching commonly take the distance among the concept nodes within a sub-structure into account when evaluating the concept similarity across different schemas. We present an encoding strategy to efficiently enumerate candidate subtrees taking the distance of nodes relative to the root of the subtree into account. The algorithm is applied to both synthetic and real-world datasets, and the experimental results demonstrate the correctness and effectiveness of the proposed technique

Mining user-generated comments

International audience—Social-media websites, such as newspapers, blogs, and forums, are the main places of generation and exchange of user-generated comments. These comments are viable sources for opinion mining, descriptive annotations and information extraction. User-generated comments are formatted using a HTML template, they are therefore entwined with the other information in the HTML document. Their unsupervised extraction is thus a taxing issue – even greater when considering the extraction of nested answers by different users. This paper presents a novel technique (CommentsMiner) for unsupervised users comments extraction. Our approach uses both the theoretical framework of frequent subtree mining and data extraction techniques. We demonstrate that the comment mining task can be modelled as a constrained closed induced subtree mining problem followed by a learning-to-rank problem. Our experimental evaluations show that CommentsMiner solves the plain comments and nested comments extraction problems for 84% of a representative and accessible dataset, while outperforming existing baselines techniques

Mining complex structured data: Enhanced methods and applications

Conventional approaches to analysing complex business data typically rely on process models, which are difficult to construct and use. This thesis addresses this issue by converting semi-structured event logs to a simpler flat representation without any loss of information, which then enables direct applications of classical data mining methods. The thesis also proposes an effective and scalable classification method which can identify distinct characteristics of a business process for further improvements

Mining substructures in protein data

In this paper we consider the 'Prions' database that describes protein instances stored for Human Prion Proteins. The Prions database can be viewed as a database of rooted ordered labeled subtrees. Mining frequent substructures from tree databases is an important task and it has gained a considerable amount of interest in areas such as XML mining, Bioinformatics, Web mining etc. This has given rise to the development of many tree mining algorithms which can aid in structural comparisons, association rule discovery and in general mining of tree structured knowledge representations. Previously we have developed the MB3 tree mining algorithm, which given a minimum support threshold, efficiently discovers all frequent embedded subtrees from a database of rooted ordered labeled subtrees. In this work we apply the algorithm to the Prions database in order to extract the frequently occurring patterns, which in this case are of induced subtree type. Obtaining the set of frequent induced subtrees from the Prions database can potentially reveal some useful knowledge. This aspect will be demonstrated by providing an analysis of the extracted frequent subtrees with respect to discovering interesting protein information. Furthermore, the minimum support threshold can be used as the controlling factor for answering specific queries posed on the Prions dataset. This approach is shown to be a viable technique for mining protein data

XML documents clustering using a tensor space model

The traditional Vector Space Model (VSM) is not able to represent both the structure and the content of XML documents. This paper introduces a novel method of representing XML documents in a Tensor Space Model (TSM) and then utilizing it for clustering. Empirical analysis shows that the proposed method is scalable for large-sized datasets; as well, the factorized matrices produced from the proposed method help to improve the quality of clusters through the enriched document representation of both structure and content information

EvoMiner: Frequent Subtree Mining in Phylogenetic Databases

The problem of mining collections of trees to identify common patterns, called frequent subtrees (FSTs), arises often when trying to interpret the results of phylogenetic analysis. FST mining generalizes the well-known maximum agreement subtree problem. Here we present EvoMiner, a new algorithm for mining frequent subtrees in collections of phylogenetic trees. EvoMiner is an Apriori-like level-wise method, which uses a novel phylogeny-specific constant-time candidate generation scheme, an efficient fingerprinting-based technique for downward closure, and a lowest common ancestor based support counting step that requires neither costly subtree operations nor database traversal. Our algorithm achieves speed-ups of up to 100 times or more over Phylominer, the current state-of-the-art algorithm for mining phylogenetic trees. EvoMiner can also work in depth first enumeration mode, to use less memory at the expense of speed. We demonstrate the utility of FST mining as a way to extract meaningful phylogenetic information from collections of trees when compared to maximum agreement subtrees and majority rule trees --- two commonly used approaches in phylogenetic analysis for extracting consensus information from a collection of trees over a common leaf set