organizations today store enormous amounts of data, the data is useless unless it can be properly interpreted and summarized. Data mining tools help turn data into information, and eventually into knowledge [1]. The Genetic Rule and Classifier Construction Environment (GRaCCE) is a data mining tool developed

automatic tools to ease the navigation in the data and their visualization. In this paper, we present a novel approach allow-ing to build automatically blocks of similar values in a given data cube that are meant to summarize the content of the cube. Our method is based on a levelwise algorithm (a la

A Bottom -k sketch is a summary of a set of items with nonnegative weights that supports approximate query processing. A sketch is obtained by associating with each item in a ground set an independent random rank drawn from a probability distribution that depends on the weight of the item and including the items with smallest rank value. Bottom- sketches are an alternative to  -mins sketches [9], which consist of the   minimum ranked items in independent rank assignments, and of min-hash [5] sketches, where hash functions replace random rank assignments. Sketches support approximate aggregations, including weight and selectivity of a subpopulation. Coordinated sketches of multiple subsets over the same ground set support subset-relation queries such as Jaccard similarity or

Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing

ABSTRACT In this paper we investigate algorithms and lower bounds for summarization problems over a single pass data stream. In particular we focus on histogram construction and K-center clustering. We provide a simple framework that improves upon all previous algorithms on these problems

such as data streams

of probes. In order to obtain expression measures it is necessary to summarize the probe level data. Using two extensive spike-in studies and a dilution study, we developed a set of tools for assessing the effectiveness of expression measures. We found that the performance of the current version

Abstract: This paper outlines my concerns with Qualitative Data Analysis ’ (QDA) numerous remodelings of Grounded Theory (GT) and the subsequent eroding impact. I cite several examples of the erosion and summarize essential elements of classic GT methodology. It is hoped that the article

hill climbing Abstract. Conceptual clustering is an important way of summarizing and explaining data. However, the recent formulation of this paradigm has allowed little exploration of conceptual clustering as a means of improving performance. Furthermore, previous work in conceptual clustering has

of three components: elements, form, and rationale. Elements are either processing, data, or connecting elements. Form is defined in terms of the properties of, and the relationships among, the elements-- that is, the constraints on the elements. The ratio-nale provides the underlying basis