https://www.kde.cs.uni-kassel.de/hotho/pub/2006/JWS2006SemanticWebMining.pdf

Semantic Web Mining

The ability to extract knowledge from data has been the driving force of Data Mining since its inception, and of statistical modeling long before even that. Actionable knowledge often takes the form of patterns, where a set of antecedents can be used to infer a consequent. In this paper we offer a solution to the problem of comparing different sets of patterns. Our solution allows comparisons between sets of patterns that were derived from different techniques (such as different classification algorithms), or made from different samples of data (such as temporal data or data perturbed for privacy reasons). We propose using the Jaccard index to measure the similarity between sets of patterns by converting each pattern into a single element within the set. Our measure focuses on providing conceptual simplicity, computational simplicity, interpretability, and wide applicability. The results of this measure are compared to prediction accuracy in the context of a real-world data mining scenario.

/pdf/comparing-sets-of-patterns-with-the-jaccard-index-22j93iyrtf.pdf

Comparing sets of patterns with the Jaccard index

There is often a large number of association rules discovered in data mining practice, making it difficult for users to identify those that are of particular interest to them. Therefore, it is important to remove insignificant rules and prune redundancy as well as summarize, visualize, and post-mine the discovered rules. Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction provides a systematic collection of research on the summarization, presentation, and new forms of association rules for post-mining. This book presents researchers, practitioners, and academicians with tools to extract useful and actionable knowledge after discovering a large number of association rules.

Post-mining of Association Rules: Techniques for Effective Knowledge Extraction

Identifying customer segments and tracking their change over time is an important application for enterprises who need to understand what their customers expect from them - now and in the future. This in particular is important for businesses which operate in dynamic markets with customers who, driven by new innovations and competing products, have highly changing demands and attitudes. Customer segmentation is typically done by applying some form of cluster analysis to obtain a set of segments to which future customers are assigned to. In this paper, we present a system for customer segmentation which accounts for the dynamics of today's markets. It employs an approach based on the discovery of frequent itemsets and the analysis of their change over time which, finally, results in a change-based notion of segment interestingness. Our approach allows us to detect arbitrary segments and analyse their temporal development. Thereby, our approach is assumption-free and pro-active and can be run continuously. Newly discovered segments or relevant changes will be reported automatically based on the application of several interestingness measures.

Mining changing customer segments in dynamic markets

Designing a new application of knowledge discovery is a very tedious task. The success is determined to a great extent by an adequate example representation. The transformation of given data to the example representation is a matter of feature generation and selection. The search for an appropriate approach is difficult. In particular, if time data are involved, there exist a large variety of how to handle them. Reports on successful cases can provide case designers with a guideline for the design of new, similar cases. In this paper we present a complete knowledge discovery process applied to insurance data. We use the TF/IDF representation from information retrieval for compiling time-related features of the data set. Experimental reasults show that these new features lead to superior results in terms of accuracy, precision and recall. A heuristic is given which calculates how much the feature space is enlarged or shrinked by the transformation to TF/IDF.

/pdf/analysing-customer-churn-in-insurance-data-a-case-study-badqnz4cla.pdf

Analysing customer Churn in insurance data: a case study

In this article, we introduce a general framework for monitoring patterns and detecting interesting changes without continuously mining the data. Using our approach, the effort spent on data mining can be drastically reduced while the knowledge extracted from the data is kept up to date. Our methodology is based on a temporal representation for patterns, in which both the content and the statistics of a pattern are modeled. We divide the KDD process into two phases. In the first phase, data from the first period is mined and interesting rules and patterns are identified. In the second phase, using the data from subsequent periods, statistics of these rules are extracted in order to decide whether or not they still hold. We applied this technique in a case study on mining mail log data. Our results show that a minimal set of patterns reflecting the invariant properties of the dataset can be identified, and that interesting changes to the population can be recognized indirectly by monitoring a subset of the patterns found in the first phase.

Efficient monitoring of patterns in data mining environments

With the ongoing shift from off-line to on-line business processes, the Web has become an important business platform, and for most companies it is crucial to have an on-line presence which can be used to gather information about their products and/or services. However, in many cases there is a difference between the intended and the effective usage of a web site and, presently, many web site operators analyse the usage of their sites to improve their usability. But particularly in the context of the Internet, content and structure change rather quickly, and the way a web site is used may change often, either due to changing information needs of its visitors, or due to an evolving user group. Therefore, the discovered usage patterns need to be updated continuously to always reflect the actual behaviour of the visitors. In this article, we introduce PAM, an automated Pattern Monitor, which can be used to observe changes to the behaviour of a web site's visitors. It is based on a temporal representation of rules in which both the content of the rule and its statistical properties are modelled. It observes pattern change as evolution of the statistical measurements captured for a rule throughout its entire lifetime and notifies the user about interesting changes within the rule base. We present PAM in a case study on the evolution of web usage patterns. In particular, we discovered association rules from a web-server log that show which pages tend to be visited within the same user session. These patterns have been imported into the monitor, and their evolution throughout a period of 8 months has been analysed. Our results show that PAM is particularly suitable to gain insights into the changes of a rule base over time.

Monitoring the evolution of Web usage patterns

Efficient Monitoring of Patterns in Data Mining Environments

In the last years the datasets available have grown tremendously, and the development of efficient and scalable data mining algorithms has become a major research challenge. However, since the data is more dynamic than static there is also a strong need to update previously discovered rules and patterns. Recently, a couple of studies have emerged dealing with the topic of incremental update of discovered knowledge. These studies mostly concentrate on the question whether new rules emerge or old ones become extinct.

We present a framework that enables the analyst to monitor the changes a rule may undergo when the dataset the rules were discovered from is updated, and to observe emerging trends as data change. We propose a generic rule model that distinguishes between different types of pattern changes, and provide formal definitions for these. We present our approach in a case study on the evolution of web usage patterns. These patterns have been stored in a database and are used to observe the mining sessions as snapshots across the time series of a patterns lifetime.

Monitoring Change in Mining Results

We elaborate on the subject of pattern change as a result of population evolution. We provide an overview of literature threads relevant to this subject, where the focus is on related works in the area of pattern adaptation rather than on modelling or understanding change. We then describe our temporal model for patterns as evolving objects and propose criteria to capture the interestingness of pattern change. We also present heuristics that trace interesting changes.

Temporal evolution and local patterns

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