Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths

Abstract

Modern web clickstream data consists of long, high-dimensional sequences of multivariate events, making it difficult to analyze. Following the overarching principle that the visual interface should provide information about the dataset at multiple levels of granularity and allow users to easily navigate across these levels, we identify four levels of granularity in clickstream analysis: patterns, segments, sequences and events. We present an analytic pipeline consisting of three stages: pattern mining, pattern pruning and coordinated exploration between patterns and sequences. Based on this approach, we discuss properties of maximal sequential patterns, propose methods to reduce the number of patterns and describe design considerations for visualizing the extracted sequential patterns and the corresponding raw sequences. We demonstrate the viability of our approach through an analysis scenario and discuss the strengths and limitations of the methods based on user feedback.

Characterizing Exploratory Visual Analysis: A Literature Review and Evaluation of Analytic Provenance in Tableau

“…Though many projects consider how to display history directly to users [HMSA08], past work lacks a characterization of the structure of exploratory analysis graphs across a range of conditions (e.g., tasks and datasets). Behavior graphs are also used in web browsing and click stream analysis [CPVDW∗01, WHS∗02, LWD∗17, New72]. We leverage past work by similarly visualizing behavior graphs in Section 6, contributing a structural signature for analysis sessions, through which differences in analysis strategies can be measured during EVA.…”

Segmentifier: Interactive Refinement of Clickstream Data

“…A sequence is a list of time‐ordered actions, indicating the activity of a specific consumer over some specific time interval. Clickstream datasets, which track every user action, can contain millions of sequences in real‐world logs, a scale much larger than many other types of event sequence data [LWD∗17, SZD∗16]. Moreover, they are inherently extremely noisy with high variability between sequences, so very few are identical.…”

“…Post‐Export: Specific Techniques A significant amount of research in this field proposes specific techniques for relatively clean and small sets of event sequences; we consider these to be useful for downstream analysis only after the export stage of our framework. Major categories of such techniques include clustering [WZT∗16,WSSM12,GXZ∗ 18,ZBS16], pattern mining [PW14, LKD∗17,LWD∗17], and cohort comparison [ZLD∗15,MSD∗16]. In all of these cases, the techniques do not handle the scale and complexity of real‐world clickstream data.…”

“…In all of these cases, the techniques do not handle the scale and complexity of real‐world clickstream data. They are validated with input data such as manually refined datasets [WSSM12,ZLD∗ 15], very small datasets [PW14, LWD∗17], or explicitly indicate a requirement for sampling [LKD∗ 17]. Segmentifier is designed to maximize the effectiveness of these techniques by allowing analysts to first refine the data to meet their requirements.…”

Chronodes

“…Acknowledging the potential of frequent sequence mining in dense event-based data, Click-stream [22] mines frequent sequences of events and visualizes them in clusters according to their similarity. In a more consolidated visual format, ActiviTree [37] renders frequent event patterns found throughout participant data as a visual tree of “common” event sequences.…”