Mining Association Rules from Relational Data – Average Distance Based Method

Bartík, Vladimír; Zendulka, Jaroslav

doi:10.1007/978-3-540-39964-3_48

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…As indicated in Figure , in the pretreatment stage, bitstream data are captured and conducted by preamble identification to split data stream into data frames. Preamble is a pseudo random sequence for synchronizing transmitter and receiver to maintain the timing relationship between them. The frame synchronization consists of two parts: sealing frame of transmitter and frame detection of receiver.…”

Section: Identification Methods Of Unknown Protocolmentioning

confidence: 99%

An association analysis and identification for unknown protocol of bitstream oriented

Zheng

2016

Concurrency and Computation

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SUMMARYAs for the bitstream data features in mobile wireless network, a method based on association rules was proposed to identify the unknown protocol in certain circumstances. The method improved traditional protocol identification technology to reduce its limitation by ways of port number and fixed features of known protocol. Through capturing bitstream data transmitted in wireless environment, the method extracted the feature information and also mined the association rules to identify and mark the unknown protocol using the learning mechanism of machine. Then the unknown protocol in a specific environment was found and analyzed by marking the fingerprint information of protocol. Finally, the efficiency of the method was evaluated by two kinds of protocol to obtain that the average recognition rate of protocol was more than 99%, while the average false-recognition rate is less than 0.6%.

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Section: Identification Methods Of Unknown Protocolmentioning

confidence: 99%

An association analysis and identification for unknown protocol of bitstream oriented

Zheng

2016

Concurrency and Computation

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Association based classification for relational data and its use in web mining

Bartík

2009

2009 IEEE Symposium on Computational Intelligence and Data Mining

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Distance-Based Methods for Association Rule Mining

Bartík

2009

Encyclopedia of Data Warehousing and Mining, Second Edition

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Association rules are one of the most frequently used types of knowledge discovered from databases. The problem of discovering association rules was first introduced in (Agrawal, Imielinski & Swami, 1993). Here, association rules are discovered from transactional databases –a set of transactions where a transaction is a set of items. An association rule is an expression of a form A?B where A and B are sets of items. A typical application is market basket analysis. Here, the transaction is the content of a basket and items are products. For example, if a rule milk ? juice ? coffee is discovered, it is interpreted as: “If the customer buys milk and juice, s/he is likely to buy coffee too.” These rules are called single-dimensional Boolean association rules (Han & Kamber, 2001). The potential usefulness of the rule is expressed by means of two metrics – support and confidence. A lot of algorithms have been developed for mining association rules in transactional databases. The best known is the Apriori algorithm (Agrawal & Srikant, 1994), which has many modifications, e.g. (Kotásek & Zendulka, 2000). These algorithms usually consist of two phases: discovery of frequent itemsets and generation of association rules from them. A frequent itemset is a set of items having support greater than a threshold called minimum support. Association rule generation is controlled by another threshold referred to as minimum confidence. Association rules discovered can have a more general form and their mining is more complex than mining rules from transactional databases. In relational databases, association rules are ordinarily discovered from data of one table (it can be the result of joining several other tables). The table can have many columns (attributes) defined on domains of different types. It is useful to distinguish two types of attributes. A categorical attribute (also called nominal) has a finite number of possible values with no ordering among the values (e.g. a country of a customer). A quantitative attribute is a numeric attribute, domain of which is infinite or very large. In addition, it has an implicit ordering among values (e.g. age and salary of a customer). An association rule (Age = [20…30]) ? (Country = “Czech Rep.”) ? (Salary = [1000$...2000$]) says that if the customer is between 20 and 30 and is from the Czech Republic, s/he is likely to earn between 1000$ and 2000$ per month. Such rules with two or more predicates (items) containing different attributes are also called multidimensional association rules. If some attributes of rules are quantitative, the rules are called quantitative association rules (Han & Kamber, 2001). If a table contains only categorical attributes, it is possible to use modified algorithms for mining association rules in transactional databases. The crucial problem is to process quantitative attributes because their domains are very large and these algorithms cannot be used. Quantitative attributes must be discretized. This article deals with mining multidimensional association rules from relational databases, with main focus on distance-based methods. One of them is a novel method developed by the authors.

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Mining Association Rules from Relational Data – Average Distance Based Method

Cited by 3 publications

References 11 publications

An association analysis and identification for unknown protocol of bitstream oriented

An association analysis and identification for unknown protocol of bitstream oriented

Association based classification for relational data and its use in web mining

Distance-Based Methods for Association Rule Mining

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