Myra Spiliopoulou

A survey of temporal knowledge discovery paradigms and methods

With the increase in the size of data sets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, interest in temporal databases has been increasing and a growing number of both prototype and implemented systems are using an enhanced temporal understanding to explain aspects of behavior associated with the implicit time-varying nature of the universe. This paper investigates the confluence of these two areas, surveys the work to date, and explores the issues involved and the outstanding problems in temporal data mining.

A bibliography of temporal, spatial and spatio-temporal data mining research

With the growth in the size of datasets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, a greater recognition of the value of temporal and spatial data has been evident and the first papers looking at the confluence of these two areas are starting to emerge. This short paper provides a few comments on this research and provides a bibliography of relevant research papers investigating temporal, spatial and spatio-temporal data mining.

MONIC: modeling and monitoring cluster transitions

There is much recent work on detecting and tracking change in clusters, often based on the study of the spatiotemporal properties of a cluster. For the many applications where cluster change is relevant, among them customer relationship management, fraud detection and marketing, it is also necessary to provide insights about the nature of cluster change: Is a cluster corresponding to a group of customers simply disappearing or are its members migrating to other clusters? Is a new emerging cluster reflecting a new target group of customers or does it rather consist of existing customers whose preferences shift? To answer such questions, we propose the framework MONIC for modeling and tracking of cluster transitions. Our cluster transition model encompasses changes that involve more than one cluster, thus allowing for insights on cluster change in the whole clustering. Our transition tracking mechanism is not based on the topological properties of clusters, which are only available for some types of clustering, but on the contents of the underlying data stream. We present our first results on monitoring cluster transitions over the ACM digital library.

Analysis of navigation behaviour in web sites integrating multiple information systems

Abstract. The analysis of web usage has mostly focused on sites composed of conventional static pages. However, huge amounts of information available in the web come from databases or other data collections and are presented to the users in the form of dynamically generated pages. The query interfaces of such sites allow the specification of many search criteria. Their generated results support navigation to pages of results combining cross-linked data from many sources. For the analysis of visitor navigation behaviour in such web sites, we propose the web usage miner (WUM), which discovers navigation patterns subject to advanced statistical and structural constraints. Since our objective is the discovery of interesting navigation patterns, we do not focus on accesses to individual pages. Instead, we construct conceptual hierarchies that reflect the query capabilities used in the production of those pages. Our experiments with a real web site that integrates data from multiple databases, the German SchulWeb, demonstrate the appropriateness of WUM in discovering navigation patterns and show how those discoveries can help in assessing and improving the quality of the site.

Web Mining

The ease and speed with which business transactions can be carried out over the Web has been a key driving force in the rapid growth of electronic commerce. In addition, customer interactions, including personalized content, e-mail campaigns, online customer service, and online surveys provide new channels of communication that were not previously available or were very inefficient. The Web is revolutionizing the way businesses interact with each other (B2B) and with each customer (B2C). It has introduced entirely new ways of doing commerce, including auctions and reverse auctions, micro-segmented offers, dynamic pricing, and up-to-date content. It also made it imperative for organizations and companies to optimize their electronic business. Knowledge about the customer is fundamental for the establishment of viable e-commerce solutions. As described by Jeff Bezos, CEO of Amazon.com, and mentioned by Joseph Pine in his book The Experience Economy (Pine et al.), customer experience is the key to building customer loyalty in an on-line store because leaving the store is only one click away. Web mining for e-commerce is the application of mining techniques to acquire this knowledge for improving e-commerce. The use of mining techniques in e-commerce can help improve cross-sells, up-sells, assortments shown, ads shown. In addition, clickstream collection allows for unprecedented measurement of site activities, conversion rates, and the effect of action (Kohavi, 2001). WEBKDD 2000 was the second workshop, 1 held in conjunction with the Sixth ACM SIGKDD International Conference on Knowledge Discovery in Databases (KDD), dedicated to the challenges of web mining. In response to call for papers, WEBKDD 2000

Mining and Visualizing the Evolution of Subgroups in Social Networks

A social network consists of people who interact in some way such as members of online communities sharing information via the WWW. To learn more about how to facilitate community building e.g. in organizations, it is important to analyze the interaction behavior of their members over time. So far, many tools have been provided that allow for the analysis of static networks and some for the temporal analysis of networks - however only on the vertex and edge level. In this paper we propose two approaches to analyze the evolution of two different types of online communities on the level of subgroups. The first method consists of statistical analyses and visualizations that allow for an interactive analysis of subgroup evolutions in communities that exhibit a rather membership structure. The second method is designed for the detection of communities in an environment with highly fluctuating members. For both methods, we discuss results of experiments with real data from an online student community

Advances in Web Mining and Web Usage Analysis

Adaptive Website Design Using Caching Algorithms.- Incorporating Usage Information into Average-Clicks Algorithm.- Nearest-Biclusters Collaborative Filtering with Constant Values.- Fast Categorization of Web Documents Represented by Graphs.- Leveraging Structural Knowledge for Hierarchically-Informed Keyword Weight Propagation in the Web.- How to Define Searching Sessions on Web Search Engines.- Incorporating Concept Hierarchies into Usage Mining Based Recommendations.- A Random-Walk Based Scoring Algorithm Applied to Recommender Engines.- Towards a Scalable kNN CF Algorithm: Exploring Effective Applications of Clustering.- Detecting Profile Injection Attacks in Collaborative Filtering: A Classification-Based Approach.- Predicting the Political Sentiment of Web Log Posts Using Supervised Machine Learning Techniques Coupled with Feature Selection.- Analysis of Web Search Engine Query Session and Clicked Documents.- Understanding Content Reuse on the Web: Static and Dynamic Analyses.

Open challenges for data stream mining research

Every day, huge volumes of sensory, transactional, and web data are continuously generated as streams, which need to be analyzed online as they arrive. Streaming data can be considered as one of the main sources of what is called big data. While predictive modeling for data streams and big data have received a lot of attention over the last decade, many research approaches are typically designed for well-behaved controlled problem settings, overlooking important challenges imposed by real-world applications. This article presents a discussion on eight open challenges for data stream mining. Our goal is to identify gaps between current research and meaningful applications, highlight open problems, and define new application-relevant research directions for data stream mining. The identified challenges cover the full cycle of knowledge discovery and involve such problems as: protecting data privacy, dealing with legacy systems, handling incomplete and delayed information, analysis of complex data, and evaluation of stream mining algorithms. The resulting analysis is illustrated by practical applications and provides general suggestions concerning lines of future research in data stream mining.

An Updated Bibliography of Temporal, Spatial, and Spatio-temporal Data Mining Research

Data mining and knowledge discovery have become important issues for research over the past decade. This has been caused not only by the growth in the size of datasets but also in the availability of otherwise unavailable datasets over the Internet and the increased value that organisations now place on the knowledge that can be gained from data analysis. It is therefore not surprising that the increased interest in temporal and spatial data has led also to an increased interest in mining such data. This bibliography subsumes an earlier bibliography and shows that the value of investigating temporal, spatial and spatio-temporal data has been growing in both interest and applicability.

Improving the Effectiveness of a Web Site with Web Usage Mining

For many companies, effective web presence is indispensable for their success to the global market. In recent years, several methods have been developed for measuring and improving the effectiveness of commercial sites. However, they mostly concentrate on web page design and on access analysis. In this study, we propose a methodology of assessing the quality of a web site in turning its users into customers. Our methodology is based on the discovery and comparison of navigation patterns of customers and non-customers. This comparison leads to rules on how the sites topology should be improved. We further propose a technique for dynamically adapting the site according to those rules.