Guided discovery of interesting relationships between time series clusters and metadata properties

Abstract

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, especially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process.To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Copyright 20XX ACM X-XXXXX-XX-X/XX/XX ...$10.00. ness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on clusters of time series data, we test our approach on a large real-world data set of time-oriented scientific research data, demonstrating how specific interesting views are automatically identified, supporting the analyst discovering interesting and visually understandable relationships.

FDive: Learning Relevance Models Using Pattern-based Similarity Measures

“…Visual interactive approaches for cluster evaluation and understanding were presented by Nam et al for general high-dimensional data [46] and by Ruppert et al [52] for the clustering of text documents. Sacha et al present SOMFlow [53], an exploration system that uses Self-Organizing Maps (SOM) to guide the user through an iterative cluster refinement task, leveraging the proximity-preserving property of SOMs [7,59] for clustering and data partitioning tasks. In a model creation task, the user needs to be guided towards areas of high uncertainty.…”

FuryExplorer: visual-interactive exploration of horse motion capture data

“…In particular, a selection-change event triggers the Sequence Detail List View to refresh the list of visible elements R 9 . In order to explore hidden relations between the data content (poses/gaits) and the metadata (like horses' names, health/lameness status, or variation of hoof motion), 39,40 the Meta Data Viewer lists available metadata attributes, as can be seen on the right-hand side of Figure 4. In this example, variation within the movement of the feet of four different horses is illustrated by colored bar charts.…”

VisInfo: a digital library system for time series research data based on exploratory search—a user-centered design approach

“…A metadatabased clustering approach is presented in [65]. In [8,9], and [10], clustering was used to gain insight into interesting relationships between the data content and metadata associated to the research data content. Visual query definition concepts for time series data are reviewed in [5], a comprehensive survey for the visualization of time series is presented in [2].…”

“…Thereby, users can compare the similarity between data level and metadata level, potentially finding interesting cross-relationships between both data aspects. A second analytical service relates to the identification of strong correlations between clusters of time series and frequent metadata items [9]. For example, consider the set of locations at which the member time series within a given cluster of time series have been measured.…”