A data clustering algorithm for mining patterns from event logs. Vaarandi, R. In *Proceedings of the 2003 IEEE Workshop on IP Operations and Management IPOM*, pages 119--126, 2003. Citeseer.

Paper abstract bibtex

Paper abstract bibtex

Clustering; Frequent Patterns; Log Analysis

@inproceedings{Vaarandi2003, abstract = {Clustering; Frequent Patterns; Log Analysis}, notes = { Domain of Application Any ascii based log-file Data Model Any type of log data Key Ideas: 1. Mining infrequent patterns is as as frequent patterns. 2. Log files donot have stucture and thus difficult to apply association rule algorithms for detecting temporal associations. 3. Log file lines can be viewed as points from a categorical data set, since each line can be divided into words, with the n-th word serving as a value for the n-th attribute. 4. One approach to Measuring distances between categorical data : Jaccard Coefficient 5. It is meaningless to discover clusters in high dimensional data space and thus measuring distances. 6. Algorithms for high dimensional data clustering:MAFIA CACTUS PROCLUS Apriori 7. Instead, their approach is density based, where a clustering algorithm tries to identify dense regions in the data space, and forms clusters from those regions. 8. }, author = {Vaarandi, Risto}, booktitle = {Proceedings of the 2003 IEEE Workshop on IP Operations and Management IPOM}, file = {:media/extstor2/knobase/papers/Vaarandi/Proceedings of the 2003 IEEE Workshop on IP Operations and Management IPOM/Vaarandi - A data clustering algorithm for mining patterns from event logs - 2003.pdf:pdf}, keywords = {Log Analysis,data clustering,data mining,system monitoring}, keywords= {Log Analysis}, pages = {119--126}, publisher = {Citeseer}, title = {{A data clustering algorithm for mining patterns from event logs}}, url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.111.820\&rep=rep1\&type=pdf}, year = {2003} }

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