A Search Space Reduced Algorithm for Mining Frequent Patterns

[[abstract]]Mining frequent patterns is to discover the groups of items appearing always together excess of a user specified threshold. Many approaches have been proposed for mining frequent patterns by applying the FP-tree structure to improve the efficiency of the FP-Growth algorithm which needs to recursively construct sub-trees. Although these approaches do not need to recursively construct many sub-trees, they also suffer the problem of a large search space, such that the performances for the previous approaches degrade when the database is massive or the threshold for mining frequent patterns is low. In order to reduce the search space and speed up the mining process, we propose an efficient algorithm for mining frequent patterns based on frequent pattern tree. Our algorithm generates a subtree for each frequent item and then generates candidates in batch from this sub-tree. For each candidate generation, our algorithm only generates a small set of candidates, which can significantly reduce the search space. The experimental results also show that our algorithm outperforms the previous approaches.[[incitationindex]]SCI[[booktype]]紙

Predictive Modelling of Retail Banking Transactions for Credit Scoring, Cross-Selling and Payment Pattern Discovery

Evaluating transactional payment behaviour offers a competitive advantage in the modern payment ecosystem, not only for confirming the presence of good credit applicants or unlocking the cross-selling potential between the respective product and service portfolios of financial institutions, but also to rule out bad credit applicants precisely in transactional payments streams. In a diagnostic test for analysing the payment behaviour, I have used a hybrid approach comprising a combination of supervised and unsupervised learning algorithms to discover behavioural patterns. Supervised learning algorithms can compute a range of credit scores and cross-sell candidates, although the applied methods only discover limited behavioural patterns across the payment streams. Moreover, the performance of the applied supervised learning algorithms varies across the different data models and their optimisation is inversely related to the pre-processed dataset. Subsequently, the research experiments conducted suggest that the Two-Class Decision Forest is an effective algorithm to determine both the cross-sell candidates and creditworthiness of their customers. In addition, a deep-learning model using neural network has been considered with a meaningful interpretation of future payment behaviour through categorised payment transactions, in particular by providing additional deep insights through graph-based visualisations. However, the research shows that unsupervised learning algorithms play a central role in evaluating the transactional payment behaviour of customers to discover associations using market basket analysis based on previous payment transactions, finding the frequent transactions categories, and developing interesting rules when each transaction category is performed on the same payment stream. Current research also reveals that the transactional payment behaviour analysis is multifaceted in the financial industry for assessing the diagnostic ability of promotion candidates and classifying bad credit applicants from among the entire customer base. The developed predictive models can also be commonly used to estimate the credit risk of any credit applicant based on his/her transactional payment behaviour profile, combined with deep insights from the categorised payment transactions analysis. The research study provides a full review of the performance characteristic results from different developed data models. Thus, the demonstrated data science approach is a possible proof of how machine learning models can be turned into cost-sensitive data models

On Pattern Mining in Graph Data to Support Decision-Making

In recent years graph data models became increasingly important in both research and industry. Their core is a generic data structure of things (vertices) and connections among those things (edges). Rich graph models such as the property graph model promise an extraordinary analytical power because relationships can be evaluated without knowledge about a domain-specific database schema. This dissertation studies the usage of graph models for data integration and data mining of business data. Although a typical company's business data implicitly describes a graph it is usually stored in multiple relational databases. Therefore, we propose the first semi-automated approach to transform data from multiple relational databases into a single graph whose vertices represent domain objects and whose edges represent their mutual relationships. This transformation is the base of our conceptual framework BIIIG (Business Intelligence with Integrated Instance Graphs). We further proposed a graph-based approach to data integration. The process is executed after the transformation. In established data mining approaches interrelated input data is mostly represented by tuples of measure values and dimension values. In the context of graphs these values must be attached to the graph structure and aggregated measure values are graph attributes. Since the latter was not supported by any existing model, we proposed the use of collections of property graphs. They act as data structure of the novel Extended Property Graph Model (EPGM). The model supports vertices and edges that may appear in different graphs as well as graph properties. Further on, we proposed some operators that benefit from this data structure, for example, graph-based aggregation of measure values. A primitive operation of graph pattern mining is frequent subgraph mining (FSM). However, existing algorithms provided no support for directed multigraphs. We extended the popular gSpan algorithm to overcome this limitation. Some patterns might not be frequent while their generalizations are. Generalized graph patterns can be mined by attaching vertices to taxonomies. We proposed a novel approach to Generalized Multidimensional Frequent Subgraph Mining (GM-FSM), in particular the first solution to generalized FSM that supports not only directed multigraphs but also multiple dimensional taxonomies. In scenarios that compare patterns of different categories, e.g., fraud or not, FSM is not sufficient since pattern frequencies may differ by category. Further on, determining all pattern frequencies without frequency pruning is not an option due to the computational complexity of FSM. Thus, we developed an FSM extension to extract patterns that are characteristic for a specific category according to a user-defined interestingness function called Characteristic Subgraph Mining (CSM). Parts of this work were done in the context of GRADOOP, a framework for distributed graph analytics. To make the primitive operation of frequent subgraph mining available to this framework, we developed Distributed In-Memory gSpan (DIMSpan), a frequent subgraph miner that is tailored to the characteristics of shared-nothing clusters and distributed dataflow systems. Finally, the results of use case evaluations in cooperation with a large scale enterprise will be presented. This includes a report of practical experiences gained in implementation and application of the proposed algorithms