Interactive exploration of interesting findings in the Telecommunication Network Alarm Sequence Analyzer TASA

In this paper we describe the final version of a knowledge discovery system, Telecommunication Network Alarm Sequence Analyzer (TASA), for telecommunication networks alarm data analysis. The system is based on the discovery of recurrent, temporal patterns of alarms in databases; these patterns, episode rules, can be used in the construction of real-time alarm correlation systems. Also association rules are used for identifying relationships between alarm properties. TASA uses a methodology for knowledge discovery in databases (KDD) where one first discovers large collections of patterns at once, and then performs interactive retrievals from the collection of patterns. The proposed methodology suits very well such KDD formalisms as association and episode rules, where large collections of potentially interesting rules can be found efficiently. When searching for the most interesting rules, simple threshold-like restrictions, such as rule frequency and confidence may satisfy a large number of rules. In TASA, this problem can be alleviated by templates and pattern expressions that describe the form of rules that are to be selected or rejected. Using templates the user can flexibly specify the focus of interest, and also iteratively refine it. Different versions of TASA have been in prototype use in four telecommunication companies since the beginning of 1995. TASA has been found useful in, e.g. finding long-term, rather frequently occurring dependencies, creating an overview of a short-term alarm sequence, and evaluating the alarm data base consistency and correctness. # 1999 Elsevier Science B.V. All rights reserved

Deducing bounds on the support of itemsets

Mining Frequent Itemsets is the core operation of many data mining algorithms. This operation however, is very data intensive and sometimes produces a prohibitively large output. In this paper we give a complete set of rules for deducing tight bounds on the support of an itemset if the supports of all its subsets are known. Based on the derived bounds [l,u] on the support of a candidate itemset I, we can decide not to access the database to count the support of I if l is larger than the support threshold (I will certainly be frequent), or if u is below the threshold (I will certainly fail the frequency test). We can also use the deduction rules to reduce the size of an adequate representation of the collection of frequent sets; all itemsets I with bounds [l,u], where l =u, do not need to be stored explicitly. To assess the usability in practice, we implemented the deduction rules and we present experiments on real-life data sets

Efficient Rule Retrieval and Postponed Restrict Operations for Association Rule Mining

Knowledge discovery in databases is a complex, iterative, and highly interactive process. When mining for association rules, typically interactivity is largely smothered by the execution times of the rule generation algorithms. Our approach is to accept a single, possibly expensive run, but all subsequent mining queries are supposed to be answered interactively by accessing a sophisticated rule cache. However there are two critical aspects. First, access to the cache must be efficient and comfortable. Therefore we enrich the basic association mining framework by descriptions of items through application dependent attributes. Furthermor

A tool for extracting XML association rules

The recent success of XML as a standard to represent semi-structured data, and the increasing amount of available XML data, pose new challenges to the data mining community. In this paper we present the XMINE operator a tool we developed to extract XML association rules for XML documents. The operator, that is based on XPath and inspired by the syntax of XQuery, allows us to express complex mining tasks, compactly and intuitively. XMINE can be used to specify indifferently (and simultaneously) mining tasks both on the content and on the structure of the data, since the distinction in XML is slight