Automatic discovery of personal action contexts - CiteSeerX

81 WS MRC - HCP-2008 - Third International Conference on Human Centered Processesand previously separated words (verbs), resource related items are embeddedwithin non-structured object descriptions and they may be represented by nounor noun phrases.Identifying time-related keywords. The date(s) when contexts are typicallyactive refer to the date interval(s) grouping the greatest number of actions. Thedefinition of this keyword will vary according criteria defined by both the observersand observed subjects. In the example, this keyword reflects the executiondate of all actions (1-2 Apr). The commitments handled are directly related tothe typical actions. Being request, a recurrent action within this context, it isinferred that the reply-to-request commitment had to be frequently handled.4 Using Clustering to Discover Personal ContextsClustering is a data mining technique applied to discover groups of related dataobjects based on attribute similarity within data sets, without any prior knowledgeof the group definition [12]. Clustering techniques apply when instances areto be divided in natural groups. These clusters presumably reflect some mechanismat work in the domain from which instances are drawn, causing someinstances to bear a stronger resemblance to one another than with the remaininginstances. Since the clustering underlying mechanisms are rarely known, thechoice of clustering approaches is usually dictated by the available tools.There are three main clustering methods; (1) Nearest Neighbor rule knownas K-means, (2) incremental and (3) statistical clustering. Statistical or probabilisticclustering assigns instances to clusters probabilistically. Probabilisticclustering supposes identifying the probability density functions of data source.Each distribution governs the attribute values of a different cluster. An efficientrepresentation of the probability density function is the mixture model, whichasserts that the data is a combination of k individual component densities, correspondingto the k clusters [13]. When the class of an instance is known, the clusterdistribution gives the probability of an instance having a certain attribute valueset. Since data records may belong to all k clusters but with different probability,the mixture model allows overlapping clusters. A well-known implementation ofthe mixture model is the Expectation-Maximization (EM) algorithm [13]. Dueto its probabilistic nature, arbitrary shaped clustering can be effectively representedby the choice of suitable component density functions (poisson, sphericalor non-spherical Gaussians for numeric attributes, and binomial or multinomialdistribution for categorical or discrete data).4.1 Case StudyThe usage of clustering in discovering contexts was tested in a case involvinga software development team of a commercial bank. The team was integratedby 4 programmers (Gonçalo, Carla, Catarina, Alexandre) and the project leader(Mariana). During the observation, the team worked on applications to manageSuppliers, Claims, Mail, Evictions, andMarketing Campaigns. In this case, a