A Clustering Comparison Measure Using Density Profiles and its Application to the Discovery of Alternate Clusterings

Data clustering is a fundamental and very popular method of data analysis. Its subjective nature, however, means that different clustering algorithms or different parameter settings can produce widely varying and sometimes conflicting results. This has led to the use of clustering comparison measures to quantify the degree of similarity between alternative clusterings. Existing measures, though, can be limited in their ability to assess similarity and sometimes generate unintuitive results. They also cannot be applied to compare clusterings which contain different data points, an activity which is important for scenarios such as data stream analysis. In this paper, we introduce a new clustering similarity measure, known as ADCO, which aims to address some limitations of existing measures, by allowing greater flexibility of comparison via the use of density profiles to characterize a clustering. In particular, it adopts a ‘data mining style’ philosophy to clustering comparison, whereby two clusterings are considered to be more similar, if they are likely to give rise to similar types of prediction models. Furthermore, we show that this new measure can be applied as a highly effective objective function within a new algorithm, known as MAXIMUS, for generating alternate clusterings

Weight control device using bites detection

The present invention relates to a device that can be used in individual weight control protocols that is capable of detecting in real time information with regard to number of bites taken, time between bites, and so forth. The weight control device can detect bites through motion detection via a sensor worn on the wrist or hand of a user. The device can include notification capabilities that can alert a user as to excessive eating speed, excessive amounts of food intake, and the like so as to provide immediate feedback for purposes of weight control