Effectively managing the data generated by Large-area Community driven Sensor Networks (LCSNs) is a new and challenging problem. One important step for managing and querying such sensor network data is to create abstractions of the data in the form of models. These models can then be stored, retrieved, and queried, as required. In our OpenSense project, we advocate an adaptive model-cover drive...

The k-means algorithm is often used in clustering applications but its usage requires a complete data matrix. Missing data, however, common many applications. Mainstream approaches to missing reduce the problem formulation through either deletion or imputation these solutions may incur significant costs. Our k-POD method presents simple extension of for that works even when missingness mechanis...

Correlation study is at the heart of time-varying multivariate volume data analysis and visualization. In this paper, we study hierarchical clustering of volumetric samples based on the similarity of their correlation relation. Samples are selected from a time-varying multivariate climate data set according to knowledge provided by the domain experts. We present three different hierarchical clu...

Last time, we introduced the task of hierarchical clustering, in which we aim to produce nested clusterings that reflect the similarity between clusters. This contrasts sharply with our former discussion of “flat” or structureless clustering methods like k-means which do not model relationships between clusters. In this lecture, we will continue our discussion of the standard model-free approac...

Interval-valued data can find their practical applications in such situations as recording monthlyinterval temperatures at meteorological stations, daily interval stock prices, etc. The primary objectiveof the presented paper is to compare three different methods of fuzzy clustering for interval-valuedsymbolic data, i.e.: fuzzy c-means clustering, adaptive fuzzy c-means clustering a...

Figure 15.1: k=3 clusters with red points chosen as facilities. Consider a situation where we have n point locations and we wish to place k facilities among these points to provide some service. It is desirable to have these facilities close to the points they are serving, but the notion of “close” can have different interpretations. The k-means problem seeks to place k facilities so as to mini...

While classification rules are essential in supervised classification methods, they are not noticed well in methods of clustering. Nevertheless, some clustering techniques have clear rules of classification, while they are not obvious in other methods. This paper discusses classification rules or classification functions in the former class including K-means, fuzzy c-means, and the mixture of d...