Unsupervised Clustering of Context Data and Learning User requirements for a Mobile Device

J. A. Flanagan. Unsupervised clustering of context data and learning user requirements for a mobile device. In A. Day, B. Kokinov, D. Leake, and R. Turner, editors, Modelling and Using Context: 5th International and Interdisciplinary Conference CONTEXT 2005, Paris, France, July, Proceedings, volume 3554 of LNAI, pages 155–168. Springer-Verlag, Berlin Heidelberg, 2005. [url]

——————————–

This paper present a technique for unsupervised learning algorithm for inferring important locations to be associated to a posssible custimization of the user interface of an application. The author departes from previous studies of inferring context extending the technique in such a way that would be possible to use the method on-the-fly and without the training period, which is impeding the development of such techniques on mobile devices.

The algorithm is called K-SCM and it is based on the idea to fuse several input sources into a string. The string is then associated to a matrix for which is calculated the variation and probability of presentation of a certain node at a certain time. Using solutions from the neural networks theory, the algorithm is able to select the winning node over a certain period of time that will define the winning location to be associated with the user interaction on the phone.

Tags:

citizen power

Recently I registered an increasing interest on the subject of citizen journalism: the Globe and Mail, The Salt Lake Tribune, The Guardian and Columbia Journalism Review (among others). I “strongly” believe in this new emergence of information not driven by corporate, which I think will modify our fruition of media and TV in general.

More pasta: One of my colleague worked on this subject writing different articles on developing public expression and public opinions. Once I also looked at this possible future scenario on the crush of citizen journalism and mainstream information.

Tags: ,

Dynamic Clustering of Maps in Autonomous Agents

D. Maio, D. Maltoni, and S. Rizzi. Dynamic clustering of maps in autonomous agents. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(11):1080–1091, November 1996. [url]

———————–

This paper present two incremental heuristic algorithms to maximise the clustering by discovery, a procedure to identify clusters in a map being learned by exploration as an agent moves in the environment.

The author state that clustering an environment map does not only mean partitioning a set of landmarks scattered in space according to some distance measure, but that the topology of the feasible connections should be taken into account.

The article contains a good literature of clustering techniques for autonomous agents and the menstion for the k-means and ISODATA methods. Also, the formalism of definition of the features of the heuristic is of high value. Finally, the proposed method is compared with others using four criteria: optimality, efficiency, robustness and stability.

Tags:

Last research direction

I am working on a project called STAMPS, whose aim is to understand how to automatically compute a semantic description of a certain “place” (read “spatial cluster”) using the messages attached to a shared map as “Post-Its”.

In our application, users can share a map of the city on their mobiles. On this map they can leave annotations for other users to retrieve. We aim at supporting the information retrieval in such setting.

Practically, we are trying to cluster together messages that presents semantic similarities and using the position where these messages were attached to define the spatial cluster (see image).

Spatialclustering

Rise of the Plagiosphere

Interesting article on Technology Review on a new metaphor on our virtual surroundings: the plagiosphere. According to the author, new technologies are causing a shift in our mental ecology, one that will turn our culture into a closing frontier of ideas.

Artificial-intelligence researchers at MIT and other universities are developing techniques for identifying nonverbatim similarity between documents to make possible the detection of nonverbatim plagiarism. While the investigators may have in mind only cases of brazen paraphrase, a program of this kind can multiply the number of parallel passages severalfold.

More pasta: Turnitin [Recognized worldwide as the standard in online plagiarism prevention, Turnitin helps educators and students take full advantage of the internet’s educational potential.]

Tags: , , ,

Spatial Custering Method for Geographic Data

T. Osaragi. Spatial custering method for geographic data. Working Paper Series 41, Centre for Advanced Spatial Analysis – University College London, London, UK, 2002. [url]

————————

This paper presents a good review of methods for Spatial Clustering. Most of these do not employ statistical methods in the process of visualizing spatial data. The effect is that the results vary with the level of aggregation and with the configuration of the zoning system.

In this paper, the authors try to oppose this proposing a method considering the characteristics of the local spatial distribution of attributes. The discuss namely the question: Which places should be unified as a spatial unit in the sense of a statistical model?

Technorati Tags: