Data Mining Practical Machine Learning Tools and Techniques

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A Break in the Clouds: Towards a Cloud Definition

Convex Analysis: (pms-28)

Kaizen: The key to Japan‚s competitive success

We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a “flat” partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data.

Density-based clustering based on hierarchical density estimates

Process discovery is the learning task that entails the construction of process models from event logs of information systems. Typically, these event logs are large data sets that contain the process executions by registering what activity has taken place at a certain moment in time. By far the most arduous challenge for process discovery algorithms consists of tackling the problem of accurate and comprehensible knowledge discovery from highly flexible environments. Event logs from such flexible systems often contain a large variety of process executions which makes the application of process mining most interesting. However, simply applying existing process discovery techniques will often yield highly incomprehensible process models because of their inaccuracy and complexity. With respect to resolving this problem, trace clustering is one very interesting approach since it allows to split up an existing event log so as to facilitate the knowledge discovery process. In this paper, we propose a novel trace clustering technique that significantly differs from previous approaches. Above all, it starts from the observation that currently available techniques suffer from a large divergence between the clustering bias and the evaluation bias. By employing an active learning inspired approach, this bias divergence is solved. In an assessment using four complex, real-life event logs, it is shown that our technique significantly outperforms currently available trace clustering techniques.

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Active Trace Clustering for Improved Process Discovery

An empirical comparison of techniques for the class imbalance problem in churn prediction

In this paper, we present Fodina, a process discovery technique with a strong focus on robustness and flexibility. To do so, we improve upon and extend an existing process discovery algorithm, namely Heuristics Miner. We have identified several drawbacks which impact the reliability of existing heuristic-based process discovery techniques and therefore propose a new algorithm which is shown to be better performing in terms of process model quality, adds the ability to mine duplicate tasks, and allows for flexible configuration options.

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Fodina: A robust and flexible heuristic process discovery technique

Deep learning for credit scoring: Do or don’t?

Process mining encompasses the research area which is concerned with knowledge discovery from event logs. One common process mining task focuses on conformance checking, comparing discovered or designed process models with actual real-life behavior as captured in event logs in order to assess the “goodness” of the process model. This paper introduces a novel conformance checking method to measure how well a process model performs in terms of precision and generalization with respect to the actual executions of a process as recorded in an event log. Our approach differs from related work in the sense that we apply the concept of so-called weighted artificial negative events toward conformance checking, leading to more robust results, especially when dealing with less complete event logs that only contain a subset of all possible process execution behavior. In addition, our technique offers a novel way to estimate a process model’s ability to generalize. Existing literature has focused mainly on the fitness (recall) and precision (appropriateness) of process models, whereas generalization has been much more difficult to estimate. The described algorithms are implemented in a number of ProM plugins, and a Petri net conformance checking tool was developed to inspect process model conformance in a visual manner.

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Seppe vanden Broucke

Papers

Active Trace Clustering for Improved Process Discovery

An empirical comparison of techniques for the class imbalance problem in churn prediction

Fodina: A robust and flexible heuristic process discovery technique

Deep learning for credit scoring: Do or don’t?

Determining Process Model Precision and Generalization with Weighted Artificial Negative Events