An EM-Approach for Clustering Multi-Instance Objects

Kriegel, Hans‐Peter; Pryakhin, Alexey; Schubert, Matthias

doi:10.1007/11731139_18

Cited by 16 publications

(10 citation statements)

References 5 publications

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“…Data Mining has various goals such as classification, prediction, clustering and estimation [2]. Several models and algorithms such as genetic algorithm, Naïve Bayes classification, regression model, and hierarchical algorithm are available for each of these goals.…”

Section: Related Workmentioning

confidence: 99%

Effective mining on large databases for intrusion detection

Adinehnia

Udzir

Affendey

et al. 2014

2014 International Symposium on Biometrics and Security Technologies (ISBAST)

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Data mining is a common automated way of generating normal patterns for intrusion detection systems. In this work a large dataset is customized to be suitable for both sequence mining and association rule learning. These two different mining methods are then tested and compared to find out which one produces more accurate valid patterns for the intrusion detection system.Results show that higher detection rate is achieved when using apriori algorithm on the proposed dataset. The main contribution of this work is the evaluation of the association rule learning that can be used for further studies in the field of database intrusion detection systems.

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Section: Related Workmentioning

confidence: 99%

Effective mining on large databases for intrusion detection

Adinehnia

Udzir

Affendey

et al. 2014

2014 International Symposium on Biometrics and Security Technologies (ISBAST)

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“…Other interesting learning scenarios using MI representations include MI clustering (Zhang & Zhou, 2009;Kriegel et al, 2006), learning instance-level classifiers from bags labeled with a percentage of positive instances (Ku¨ck & de Freitas, 2005), and predicting the salience of instances in an MI regression setting (Wagstaff & Lane, 2007). None of these scenarios involves bag-level predictions, however, so we do not consider them in this paper.…”

Section: Learning In Other Supervised Settingsmentioning

confidence: 99%

A review of multi-instance learning assumptions

Foulds

Frank

2010

The Knowledge Engineering Review

322

238

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Multi-instance (MI) learning is a variant of inductive machine learning where each learning example contains a bag of instances instead of a single feature vector. The term commonly refers to the supervised setting, where each bag is associated with a label. This type of representation is a natural fit for a number of real-world learning scenarios, including drug activity prediction and image classification, hence many multi-instance learning algorithms have been proposed. Any MI learning method must relate instances to bag-level class labels, but many types of relationships between instances and class labels are possible. Although all early work in MI learning assumes a specific MI concept class known to be appropriate for a drug activity prediction domain, this "standard MI assumption" is not guaranteed to hold in other domains. Much of the recent work in MI learning has concentrated on a relaxed view of the MI problem, where the standard MI assumption is dropped, and alternative assumptions are considered instead. However, often it is not clearly stated what particular assumption is used and how it relates to other assumptions that have been proposed. In this paper, we aim to clarify the use of alternative MI assumptions by reviewing the work done in this area.

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“…However, to the best of our knowledge none of these methods deals with MI objects and the question of how to derive CAs from a set of MI objects. In [7], a method to derive MI clusters based on EM clustering was proposed. The method clusters the instances using ordinary EM clustering and afterwards uses a multinomial process to group MI objects.…”

Section: Related Workmentioning

confidence: 99%