{'fraction_non_alphanumeric': 0.015659955257270694, 'fraction_numerical': 0.0, 'mean_word_length': 5.347517730496454, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Visual Data Mining of Web Navigational Data

Discovering web navigational trends and understanding data mining results is undeniably advantageous to web designers and web-based application builders. It is also desirable to interactively investigate web access data and patterns, to allows ad-hoc discovery and examination of patterns that are not apriori known. Visualizing the usage data in the context of the web site structure is of major importance, as it puts web access requests and their connectivity in perspective. Various visualization tools have been developed for this task, but often fail to provide visual data mining functionalities to generate new patterns. Here we present our visual data mining system, WebViz, which allows interactive investigation of web usage data within their structure context, as well as ad-hoc knowledge pattern discovery on web navigational behaviour.