Name
Abstract | ||
|---|---|---|
Exploratory data mining is fundamental to fostering an appreciation of complex datasets. For large and continuously growing datasets, such as obtained by regular sampling of an organisation's communications, the exploratory phase may never finish. This paper describes a methodology for exploratory data mining within an organisational communications dataset. A model of support for knowledge discovery is described in conjunction with a communications based concept hierarchy. This is then used as the basis for a set of visualisations. The intention of supporting visualisations in this way is to establish a sound set of requirements for the representation of communications data. The visualisations provide several interconnected representations of the data, as well as support query and drill-down into a dataset. It is suggested that this interaction with the dataset facilitates an appreciation of the data which precedes and shapes knowledge discovery. A communications analysis example is developed using the visualisations within the context of exploratory data mining. |
Year | Venue | Keywords |
|---|---|---|
2008 | AusDM | communications data,concept hierarchy,complex datasets,exploratory phase,knowledge discovery,communications analysis example,exploratory mining,sound set,exploratory data mining,support query,organisational communications dataset,data mining,visualisation |
Field | DocType | Citations |
Data science,Data mining,Visualization,Computer science,Sampling (statistics),Knowledge extraction,Concept hierarchy | Conference | 0 |
PageRank | References | Authors |
0.34 | 14 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alan Allwright | 1 | 0 | 0.68 |
| John F. Roddick | 2 | 1908 | 331.20 |