Perspective Exploratory Methods for Multidimensional Data Analysis Technical practice abounds with numerous diverse data records. Sometimes the data is complete, sometimes it is censored or truncated. It is not always easy and straightforward to record the data. And even after, the data processing is by no means simple, especially when the data forms a significant set of a huge size and large informational diversity. Typically, the data containing more observed variables, either dependent or independent, is called multidimensional. Also, if the multidimensional data contains numerous records, it is not easy to determine which dependent or independent variables are important for further study. Our aim and ambition is to introduce a couple of methods which are very suitable and sometimes absolutely necessary for exploratory data analysis. The methods help us to determine i) the level of significance of the data for single recorded variables, ii) the level of mutual dependence among the data, and iii) the choice of the best representatives for further data study. The recommended methods used for the exploratory data analysis are presented with practical examples.

Summarization and Visualization of MultiLevel and MultiDimensional Itemsets Abstract   Frequent itemset (FI) mining aims at discovering relevant patterns from sets of transactions. In this work we focus on multilevel and multidimensional data, which provide a rich description of subjects through multiple features each at different levels of detail. Summarization of FIs has been only marginally addressed so far with specific reference to multilevel and multidimensional FIs. In this paper we fill this gap by proposing SUSHI, a framework for summarizing and visually exploring multilevel and multidimensional FIs. Specifically, SUSHI is based on (i) a similarity function for FIs which takes into account both their extensional (supportbased) and intensional (featurebased) natures; (ii) theoretical results concerning antimonotonicity of support and similarity in multilevel settings, which allow us to propose an efficient clustering algorithm to generate hierarchical summaries; and (iii) two integrated approaches to summary visualization and exploration: a graphbased one, which highlights intercluster relationships, and a treebased one, which emphasizes the relationships between the representative of each cluster and the other FIs in that cluster. SUSHI is evaluated using both a real and a synthetic dataset in terms of effectiveness, efficiency, and understandability of the summary, with reference to three different strategies for choosing cluster representatives. Overall, SUSHI shows to outperform previous approaches and to be a valuable tool to expedite the analysis of FIs. Besides, one of the three strategies for choosing cluster representatives shows to be the most effective one.