We describe a novel approach for clustering col-lections of sets, and its application to the analysis and mining of categorical data. By “categorical data, ” we mean tables with fields that cannot be naturally ordered by a metric- e.g., the names of producers of automobiles, or the names of prod

describe our technique for a coordinated measurement approach that combines real total power measurement with performance-counter-based, perunit power estimation. The resulting tool offers live total power measurements for Intel Pentium 4 processors, and also provides power breakdowns for 22 of the major

In this paper we propose two methods to study the problem of clustering categorical data. The first method is based on dynamical system approach. The second method is based on the graph partitioning approach. Dynamical systems approach for clustering categorical data have been studied by some

conserves, on average, 22% of the power required by a system without dynamic control while still maintaining QoS goals. Finally, we use trace-based simulations to analyze controller performance on server clusters larger than our testbed, and show how concepts from approximation theory can be used to further

-iteration computation times and an optimization problem that appears to scale well to more complex characters. We show that qualities such as squash-and-stretch that are expected from physically based optimization result from our approach. Our animation system is particularly useful for synthesizing highly dynamic

-K categories. In contrast, traditional keyword search returns the top-K documents (i.e., data items) relevant to a user query. The need to dynamically categorize new data and also update the meta-data required for fast responses to user queries poses interesting challenges. The brute force approach of updating

of the Bag-of-Systems(BoSs). We also introduce a new underwater video data set, which is composed of more than 100 hours of annotated video sequences. Our results indicate that, for the underwater habitat identification, the dynamic texture approach has multiple benefits over the traditional STIP-based video

calls for fast, incremental and robust learning algorithms. In this paper, we investigate a novel approach to spam filtering based on adaptive statistical data compression models. The nature of these models allows them to be employed as probabilistic text classifiers based on character-level or binary

Many applications require matching objects to a predefined, yet highly dynamic set of categories accompanied by category descriptions. We present a novel approach to solving this class of categorization problems by formulating it in a semi-supervised clustering framework. Text-based matching

by investors over time. For example, the growth/value category was not widely used in 1980. Instead, we impose no categorical structure on the data and just follow the flows. Most strikingly, we are able to document that the fund flow effect is highly related to the value effect, a finding that could not have