Orientation-Enhanced Parallel Coordinate Plots

Abstract

Parallel Coordinate Plots (PCPs) is one of the most powerful techniques for the visualization of multivariate data. However, for large datasets, the representation suffers from clutter due to overplotting. In this case, discerning the underlying data information and selecting specific interesting patterns can become difficult. We propose a new and simple technique to improve the display of PCPs by emphasizing the underlying data structure. Our Orientation-enhanced Parallel Coordinate Plots (OPCPs) improve pattern and outlier discernibility by visually enhancing parts of each PCP polyline with respect to its slope. This enhancement also allows us to introduce a novel and efficient selection method, the Orientation-enhanced Brushing (O-Brushing). Our solution is particularly useful when multiple patterns are present or when the view on certain patterns is obstructed by noise. We present the results of our approach with several synthetic and real-world datasets. Finally, we conducted a user evaluation, which verifies the advantages of the OPCPs in terms of discernibility of information in complex data. It also confirms that O-Brushing eases the selection of data patterns in PCPs and reduces the amount of necessary user interactions compared to state-of-the-art brushing techniques.

Smart Brushing for Parallel Coordinates

“…The number of polylines rendered are reduced to minimize clutter and reveal patterns in the data. Raidou et al present an orientation-enhanced brushing technique for PCPs [3]. In their paper, a visual description of State-of-the-Art brushing techniques is detailed ranging from probing (0-order brushing), to composite AND OR brushes (composite 1 st -order brushing).…”

“…Any intelligence involved is based solely on the users own knowledge and perception of the data. In this paper, we use the phrase 'Smart Brushing' to encompass our ideas of both [3] and summarizes the current State-of-the-Art brushing techniques. We propose the next step in these brushing interactions with higher-order brushing and smart, data-guided brushing.…”

Visual analytics: designing flexible filtering in parallel coordinate graph

“…The main strength of the graph is on its ability to reveal the relationship of multivariate data [15][16]in a single graph through its multi-dimensionalaxes. When compared to other visual technology, parallel coordinate graph is well ahead in term of time taken to analyze its data [17]. Parallel coordinate alsowork well with many of visual analytic technology such as filtering, sorting, zooming, slicing, dicing and brushing [18][19].…”

Visual Exploration of Large Multidimensional Data Using Parallel Coordinates on Big Data Infrastructure

“…This can be accomplished using density-based methods [8,12,13] or curves instead of lines [14][15][16]. However, such methods do not scale as they are, since they require to draw every item.…”

“…For hundred of thousands of items, traditional line-based parallel coordinate plot induces substantial visual clutter [7] (see Figure 1b) and interaction latencies. Several approaches have been proposed to deal with these challenges, ranging from using visual enhancement [8], via interactive tools [9], to joint representations of data subsets [10]. These techniques, either data-driven or screen-based, successfully handle up to millions of records.…”