Aaron Ceglar

Association mining

The task of finding correlations between items in a dataset, association mining, has received considerable attention over the last decade. This article presents a survey of association mining fundamentals, detailing the evolution of association mining algorithms from the seminal to the state-of-the-art. This survey focuses on the fundamental principles of association mining, that is, itemset identification, rule generation, and their generic optimizations.

Higher order mining

The value of knowledge obtainable by analysing large quantities of data is widely acknowledged. However, so-called primary or raw data may not always be available for knowledge discovery for several reasons. First, cooperating institutions that are interested in sharing knowledge may not be willing (or allowed) to disclose their primary data. Second, data in the form of streams are only temporarily available for processing. If stored at all, stream data are maintained in the form of synopses or derived, abstract representations of the original data. Finally, even for non-stream data, there are limits on the computation speed to be achieved -- such limits are set by hardware and firmware technologies. This problem can only be partially solved through parallelization and increased processing power. Ultimately, in many cases data must be summarized to be processed efficiently. In the light of these observations, we anticipate the need for defining and practising data mining without the luxury of primary data. To that end, we formally introduce the paradigm of Higher Order Mining as a form of data mining that is applied over non-primary, derived data or patterns. Although Higher Order Mining is a new paradigm, there are already research advances on knowledge discovery methods from patterns rather than data. We discuss them and organize them under the light of the new paradigm. We show that the HOM paradigm reveals further potential for knowledge discovery, including the delivery of rules and patterns with semantics that are closer to human intuition and are thus more appropriate for human inspection.

Visualising hierarchical associations

Recent association-mining research has led to the development of techniques that allow the accommodation of concept hierarchies within the mining process. This extension results in the discovery of rules which associate not only groups of items but which are also influenced by the hierarchies within which an item may reside. Given this, there then arises a need for techniques whereby such hierarchical associations can be presented to the user. Current association rule visualisation techniques are limited, as they do not effectively incorporate or enable the visualisation of hierarchical semantics. This paper presents a review of current hierarchical and association visualisation techniques and introduces a novel technique for visualising hierarchical association rules.

A new approach to collaborative frameworks using shared objects

Multi-user graphical applications currently require the creation of a set of interface objects to maintain each participating display. The concept of shared objects allows a single object instance to be used in multiple contexts concurrently. This provides a novel way of reducing collaborative overheads by requiring the maintenance of only a single set of interface objects. The paper presents the concept of a shared-object collaborative framework and illustrates how the concept can be incorporated into an existing object oriented toolkit.

Postponing schema definition: low instance-to-entity ratio (LItER) modelling

There are four classes of information system that are not well served by current modelling techniques. First, there are systems for which the number of instances for each entity is relatively low resulting in data definition taking a disproportionate amount of effort. Second, there are systems where the storage of data and the retrieval of information must take priority over the full definition of a schema describing that data. Third, there are those that undergo regular structural change and are thus subject to information loss as a result of changes to the schemas information capacity. Finally, there are those systems where the structure of the information is only partially known or for which there are multiple, perhaps contradictory, competing hypotheses as to the underlying structure. This paper presents the Low Instance-to-Entity Ratio (LItER) Model, which attempts to circumvent some of the problems encountered by these types of application. The two-part LItER modelling process possesses an overarching architecture which provides hypothesis, knowledge base and ontology support together with a common conceptual schema. This allows data to be stored immediately and for a more refined conceptual schema to be developed later. It also facilitates later translation to EER, ORM and UML models and the use of (a form of) SQL. Moreover, an additional benefit of the model is that it provides a partial solution to a number of outstanding issues in current conceptual modelling systems.

Enhancing Layout and Interaction in Formal Concept Analysis

Formal Concept Analysis (FCA) derives a multiple-inheritance class hierarchy from a formal context. The number of classes is bounded above by an exponential function of the number of objects and attributes in the context. To support interactive analysis of large formal contexts, this paper exploits a divide-and-conquer technique which discovers hierarchical structure in amenable formal contexts. That hierarchical structure is used to expedite and enhance both the layout of, and user interaction with, the concept lattice. The principal contribution is the dual use of the discovered hierarchical structure for scalable, interactive FCA.

Interfaces for discourse summarisation: a human factors analysis

Empirical studies assessing the effectiveness of novel document interfaces are becoming more prevalent, however relatively little attention has been paid to how such tools could work with less structured documents featuring multiple contributors. Participants in this study used different interfaces to answer questions requiring the exploration of collaborative discourse. User performance was influenced by an interaction of interface, transcript, and question type. Individual differences also impacted on performance with higher education levels and higher general knowledge scores being associated with better task performance. The results also revealed that unnecessary interface functionality can hinder performance.

Using Semantic Context to Rank the Results of Keyword Search

ABSTRACT In an empirical user study, we assessed two approaches to ranking the results from a keyword search using semantic contextual match based on Latent Semantic Analysis. These techniques involved searches initiated from words found in a seed document within a corpus. The first approach used the sentence around the search query in the document as context while the second used the entire document. With a corpus of 20,000 documents and a small proportion of relevant documents (<0.1%), both techniques outperformed a conventional keyword search on a recall-based information retrieval (IR) task. These context-based techniques were associated with a reduction in the number of searches conducted, an increase in users’ precision and, to a lesser extent, an increase in recall. This improvement was strongest when the ranking was based on document, rather than sentence, context. Individuals were more effective on the IR task when the lists returned by the techniques were ranked better. User performance on the task also correlated with achievement on a generalized IQ test but not on a linguistic ability test.