Cluster Optimization for Improved Web Usage Mining

Now days, World Wide Web (WWW) has become rich and most powerful source of information. Conversely, it has become tricky and critical task to retrieve actual information due to its continuous expansion in dimensions. Web Usage Mining is a step-wise technique of extracting useful access patterns of the user from web. Web personalization makes use of web usage mining techniques, for knowledge acquisition process done by analyzing the user navigational patterns. The web page personalization involves clustering of different web pages having similar navigation patterns for an individual. Since cluster size expands due to the frequent access, optimization or shrinking the size of clusters becomes a chief consideration. This paper proposes a tactic of cluster optimization based on concept of swarm intelligence techniques. Later on based on the recognition of user access patterns, clustering is implemented using neural fuzzy approach i.e. NEF Class algorithm and cluster optimization is implemented using Ant Nest Mate Approach

HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks

The unsupervised detection of anomalies in time series data has important applications in user behavioral modeling, fraud detection, and cybersecurity. Anomaly detection has, in fact, been extensively studied in categorical sequences. However, we often have access to time series data that represent paths through networks. Examples include transaction sequences in financial networks, click streams of users in networks of cross-referenced documents, or travel itineraries in transportation networks. To reliably detect anomalies, we must account for the fact that such data contain a large number of independent observations of paths constrained by a graph topology. Moreover, the heterogeneity of real systems rules out frequency-based anomaly detection techniques, which do not account for highly skewed edge and degree statistics. To address this problem, we introduce HYPA, a novel framework for the unsupervised detection of anomalies in large corpora of variable-length temporal paths in a graph. HYPA provides an efficient analytical method to detect paths with anomalous frequencies that result from nodes being traversed in unexpected chronological order.Comment: 11 pages with 8 figures and supplementary material. To appear at SIAM Data Mining (SDM 2020

Mining complex structured data: Enhanced methods and applications

Conventional approaches to analysing complex business data typically rely on process models, which are difficult to construct and use. This thesis addresses this issue by converting semi-structured event logs to a simpler flat representation without any loss of information, which then enables direct applications of classical data mining methods. The thesis also proposes an effective and scalable classification method which can identify distinct characteristics of a business process for further improvements

GUI Decoder and Enricher

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 53-54).Much information can be obtained from knowing what tasks the user does on his or her cell phone. This paper describes GuiDE, an automated user-activity recognition system on the mobile phone. GuiDE's unique approach to activity recognition exploits GUI screenshots taken as the individual interacts with their cell phone. These screenshots are aggregated into a graph to help probabilistically determine whether or not a set of screenshots can be considered a user-level activity. A frequency count of different sets of screenshots is also kept to act as a sanity check against the probabilistic result. GuiDE is just a partial step towards a much more powerful tool that can correlate GUI information with other services to provide a better understanding of user activity.by Emily Z. Yan.M.Eng