Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., Verkamo, A.I.: Finding interesting rules from large sets of discovered association rules. In: Proceedings of the Third International Conference on Information and Knowledge Management (CIKM). (1994) 401--407  Home/Search   Document Details and Download   Summary   Related Articles   Check

....large numbers of associations. This imposes a large burden on the data analyst who must determine manually which of these associations are of interest. An active area of research is the identi cation of suitable measures of interestingness that might be applied to automatically lter associations [6, 11, 12]. This is also of importance for impact rule discovery. Aumann and Lindell [3] suggested that distribution measures be used to measure interestingness for impact rules. Their examples include the deviation from that of the training set as a whole of the mean or variance of the target for the ....

M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. Verkamo. Finding interesting rules from large sets of discovered associationd rules. In Proc. 3rd Int. Conf. Information and Knowledge Management, pages 401-407, 1999.

....provides significant performance improvements on a variety of databases. 1 Introduction The output of boolean association rule mining algorithms is often too large for manual examination. For dense datasets, it is often impractical to even generate all frequent itemsets. Among recent approaches [16, 15, 9, 6, 8, 5, 4] to manage this gigantic output, the closed itemset approach [16, 15] is attractive in that both the identities and supports of all frequent itemsets can be derived completely from the frequent closed itemsets. However, the usefulness of this approach critically depends on the presence of frequent ....

....be applied after frequent itemsets have been mined. They are therefore inefficient and sometimes even infeasible because the number of frequent itemsets could be very large, especially for dense databases. Techniques for pruning uninteresting rules during mining have been previously presented in [16, 15, 9, 6, 8, 5, 4]. In most of these studies (other than those following the closed itemset approach) it is sufficient for a rule to be considered uninteresting or redundant if it has no additional predictive power over another rule with fewer items. Techniques based on the closed itemset approach [16, 15] on the ....

M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A.I. Verkamo. Finding interesting rules from large sets of discovered association rules. In Intl. Conf. on Information and Knowledge Management (CIKM), November 1994.

....the relationships are defined, such analysis often requires a suitable metric to capture the dependencies among variables. For example, metrics such as support, confidence, lift, correlation, and collective strength have been used extensively to evaluate the interestingness of association patterns [9, 14, 1, 15, 11]. These metrics are defined in terms of the frequency counts tabulated in a 22 contingency table as shown in Table 1. Unfortunately, many such metrics provide conflicting information about the interestingness of a pattern, and the best metric to use for a given application domain is rarely known. ....

M. Klemettinen, H. Mannila, P. Ronkainen, T. Toivonen, and A. Verkamo. Finding interesting rules from large sets of discovered association rules. In Proc. of the 3rd Int'l Conf. on Information and Knowledge Management (CIKM'94)., pages 401--407, Gaithersburg, Maryland, November 1994.

....rules [4] Second, it was recognized that domain knowledge may also play an important role in determining the interestingness of association rules. Therefore, a number of subjective measures of interestingness have been put forward, such as unexpectedness [13] actionability [1] and rule templates [10]. Finally, the most recent stream of research advocates the evaluation of the interestingness of associations in the light of the micro economic framework of the retailer [9] More specifically, a pattern in the data is considered interesting only to the extent in which it can be used in the ....

M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A.I. Verkamo. Finding interesting rules from large sets of discovered association rules. In Nabil R. Adam, Bharat K. Bhargava, and Yelena Yesha, editors, Proceedings of the Third International Conference on Information and Knowledge Management, pages 401--407. ACM Press, 1994.

.... the number of disjuncts in a generalized rule, i.e. the expected (or maximum) number of distinct values of each generalized attribute or the number of tuples in the generalized relation [4] More over, one may also specify some syntactic or semantic constraints on the forms of discovered rules[2, 8]. Recently, Shen et al. 10] proposed an interesting tech nique to specify the form of rules to be discovered in data mining, called metaquery, which presents a desired logical form for the rules to be discovered and serves as an important interface between human discoverers and the discovery ....

M. Klemettinen, N. Mannila, P. Ronkainen, N. Toivo- hen, and A. I. Verkamo. Finding interesting rules from large sets of discovered association rules. In Proc. 3rd Int'l Conf. on Information and Knowledge Management, pages 401 408, Gaithersburg, Maryland, Nov. 1994.

....adjusted, etc. Such tasks should be accomplished conveniently by a graphical user interface although they can be specified (but not so conveniently) using DMQL language primitives. For interactive refining of data mining results, one should display the results using rule visualization tools [12] or in different output forms, including generalized relations, projected statistical tables, bar charts, pie charts, curves, surfaces, quantitative rules, etc. This process may be helped by report writers or graphical display softwares. DMQL provides the following primitives for displaying ....

M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivo- hen, and A. I. Verkamo. Finding interesting rules from large sets of discovered association rules. In Proc. $rd Int'l Conf. on Information and Knowledge Management, pages 401 408, Gaithersburg, Maryland, Nov. 1994.

....how our approach answers the previous questions, let us examine proposed solutions for meeting such needs. 1.1 Related Work: an Outline Among approaches addressing the described issue, two main trends can be distinguished. The former provides users with mechanisms for filtering rules. In [3, 16], the user defines templates, and rules not matching with them are discarded. In [22, 29] boolean operators are introduced for selecting rules including (or not) given items. A similar approach expanded with a measure of usefulness of extracted rules, called improvement, is proposed in [5] In ....

....quality. On the other hand, execution times are reduced compared with the discovering of all association rules. Such results are proved (in the groundwork of lattice theory) and illustrated by experiments, achieved from real life datasets. Integrating reduction methods Templates, as defined in [3, 16], can directly be used for extracting from the bases all association rules matching some user specified patterns. Information in taxonomies associated with the dataset can also be integrated in the process as proposed in [14, 28] for extracting bases for generalized (multi level) association ....

M. Klemettinen, H. Mannila, P. Ron!inen, H. Toivonen, and A. I. Ver!mo. Finding interesting rules from large sets of discovered association rules. Proc. of the 3rd CIKM Conference, pages 401407, November 1994.

....the relationships are defined, such analysis often requires a suitable metric to capture the dependencies among variables. For example, metrics such as support, confidence, lift, correlation, and collective strength have been used extensively to evaluate the interestingness of association patterns [9, 14, 1, 15, 11]. These metrics are defined in terms of the frequency counts tabulated in a 2 x 2 contingency table as shown in Table 1. Unfortunately, many such metrics provide conflicting information about the interestingness of a pattern, and the best metric to use for a given application domain is rarely ....

M. Klemettinen, H. Mannila, P. Ronkainen, T. Toivonen, and A. Verkamo. Finding interesting rules from large sets of discovered association rules. In Proc. of the 3rd Int'l Conf. on Information and Knowledge Management (CIKM'9)., pages 401 407, Gaithersburg, Maryland, November 1994.

....the performance guarantee. I Introduction Since its introduction [1] the problem of mining association rules from large databases has been the subject of numerous studies. These studies cover a broad spectrum of topics including: i) fast algorithms based on the levelwise Apriori framework [3, 13], partitioning [19, 18] and sampling [24] ii) incremental updating and parallel algorithms [6, 2, 8] iii) mining of generalized and multi level rules [21, 9] iv) mining of quantitative rules [22, 16] v) mining of multidimensional rules [7, 14, 12] vi) mining rules with item constraints ....

....wants to focus the generation of rules to a specific, small subset of candidates, based on properties of the data Such a black box model would be tolerable if the turnaround time of the computation were small, e.g. a few seconds. However, despite the development of many efficient algorithms [2, 3, 6, 8, 13, 18, 19, 24], association mining remains a process typically taking hours to complete. Before a new invocation of the black box, the user is not allowed to preempt the process and needs to wait for hours. Furthermore, typically only a small fraction of the computed rules might be what the user was looking ....

[Article contains additional citation context not shown here]

M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A.I. Verkamo. Finding interesting rules from large sets of discovered association rules. CIKM 94, pp 401-408.

....Swizerland, 1995 covery of interesting association relationships among huge amounts of data will help marketing, decision making, and business management. Therefore, mining association rules from large data sets has been a focused topic in recent research into knowledge discov ery in databases [1, 2, 3, 9, 12, 14]. Studies on mining association rules have evolved from techniques for discovery of functional dependen cies [10] strong rules [14] classification rules [7, 15] causal rules [11] clustering [6] etc. to disk based, ef ficient methods for mining association rules in large sets of transaction ....

....concept level, such as the associations among particular bar codes, because of the tiny average support for each primitive data item in a very large item set. However, mining association rules at high concept levels may often lead to the rules corresponding to prior knowledge and expectations [9], such as milk bread , or lead to some uninteresting attribute combinations, such as toy milk . In order to remove uninteresting rules generated in knowledge mining processes, researchers have proposed some measurements to quantifv the usefulness or interestinghess of a rule [13] or ....

M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivo- nen, and A. I. Verkamo. Finding interesting rules from large sets of discovered association rules. In Proc. 3rd Int'l Conf. on Information and Knowledge Management, pp. 401 408, Gaithersburg, Maryland, Nov. 1994.

....that a pattern q also occurs in S when the pattern p occurs in S. Mining association rules in transaction databases has been studied extensively in recent database research, with several interesting algorithms developed for mining single or multiple level association rules in such databases [4, 22 35, 53, 27]. A similar association rule mining algorithm can be developed to adapt such rule mining technique in object oriented databases. For example, following the study in [27] a multiple level rule mining algorithm may first discover large itemsets (the set of items with a support no smaller than a ....

M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A.I. Verkamo. Finding interesting rules from large sets of discovered association rules. In Proc. 3rd Int. Conf. on Information and Knowledge Management, pages 401-408, Gaithersburg, Maryland, Nov. 1994.

....post processing methods allowing biologists to analyze very large numbers of regulation relationships among genes and select those that are of interest to them. The problem of post analysis of large numbers of discovered rules using filtering methods has been studied before in the KDD literature [1, 13, 15, 20, 22, 27, 28, 32], and we utilize some of this work in our approach. In particular, Klemettinen et al. [15] and Imielinski et al. [13] present the methods for the users to specify classes of patterns in which they are interested by providing pattern templates expressed in a certain specification language. The work ....

....select those that are of interest to them. The problem of post analysis of large numbers of discovered rules using filtering methods has been studied before in the KDD literature [1, 13, 15, 20, 22, 27, 28, 32] and we utilize some of this work in our approach. In particular, Klemettinen et al. [15] and Imielinski et al. [13] present the methods for the users to specify classes of patterns in which they are interested by providing pattern templates expressed in a certain specification language. The work of Tuzhilin and Liu [32] extends the work of [13, 15] by presenting a template language ....

[Article contains additional citation context not shown here]

Klemettinen M., Mannila H., Ronkainen P., Toivonen H., and Verkamo A.I. Finding interesting rules from large sets of discovered association rules. In Proceedings of the Third International Conference on Information and Knowledge Management, 1994.

....[14] or the datasets with highly correlated attributes. Another very common criticism of many association rule discovery algorithms is that they produce not only too many rules, but also that many of the discovered rules are spurious, trivial, or simply irrelevant to the application at hand [34, 66, 46, 71, 73, 20, 79, 78, 14]. To address this problem, most previous approaches have focused on develop ing various measures of rule interestingness that could be used to prune the non interesting rules. Alternatively, these measures could be directly incorporated into association rule discovery algorithms in order to ....

....rule validation problem in the post analysis stage of the data mining process has been addressed before. In particular, there has been work done on specifying filtering constraints that select only certain types of rules from the set of all the discovered rules; examples of this research include [46, 53, 55]. In these approaches the user specifies constraints but does not do it iteratively. In contrast to this, it has been observed by several researchers, e.g. 18, 32, 72, 67, 50, 2, 69] that knowledge discovery should be an iterative and interactive process that involves an explicit participation ....

[Article contains additional citation context not shown here]

M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. I. Verkamo. Finding interesting rules from large sets of discovered association rules. In Pro- ceedings of the Third International Conference on Information and Knowledge Management, December 1994.

....a text mining system should also identify a correlation between concepts that are not the primary focus of an individual study. 3. Related Work Identifying informative features from natural language (text) can be difficult; thus, existing approaches use semantically poor features, such as words[6 14]. This approach has the advantage of being domain independent and easy to implement; it has the disadvantage of producing the same number of attributes as the size of the vocabulary. The Apriori algorithm requires potentially 2 item sets where m is the number of terms in the vocabulary (see ....

....rather than the text mining task in section 2. Other related research has focused on constructing techniques to improve the quality of text mined association rules. Most of these approaches first generate a set of rules, and then apply pruning or ranking techniques, such as interestingness [14,21 25]. Unlike these approaches, which consider each rule individually, we focus on improving the interestingness for the set of rules. That is, on average, rules based on semantically richer representations would be higher than rules based on a semantically poorer representation. We consider ....

[Article contains additional citation context not shown here]

M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. I. Verkamo., "Finding Interesting rules from Large Sets of discovered association rules", CIKM'94, Maryland,

....prunes redundant rules and parts of the hypothesis space that cannot contain better solutions than the best ones found so far. We evaluate the performance of the algorithm (relative to the Apriori algorithm) on realistic knowledge discovery problems. 1 Introduction Association rules (e.g. [1, 5, 2]) express regularities between sets of data items in a database. Beer and TV magazine ) chips] is an example of an association rule and expresses that, in a particular store, all customers who buy beer and a TV magazine are also likely to buy chips. In contrast to classi ers, association rules ....

M. Klemettinen, H. Mannila, P. Ronkainen, H.Toivonen, and A. I. Verkamo. Finding interesting rules from large sets of discovered associacion rules. Proc. Third International Conference on Information and Knowledge Management, 1994.

....of keyword records, i.e. identifying clusters of records with similar meaning and exploiting the existence of such clusters. 119 CHAPTER 6 FRAGMENTATION APPROACH The rule based data mining paradigm has attracted much attention and many successful algorithms and systems have been developed [25, 49, 61, 62, 68, 77, 78, 79, 86, 89, 90, 115, 116]. Although most work has been done on mining centralized databases, some promising approaches to mine distributed databases have also been developed [80, 97, 101] With the growing popularity of the World Wide Web (WWW) mining distributed data sources becomes ever more important as the WWW itself ....

Klemettinen M., Mannila H., Rokainen P., Toivonen H. and Verkamo I., "Finding Interesting Rules from Large Sets of Discovered Association Rules", Proceedings of the Third International Conference on Information and Knowledge Management (CIKM'94), pp. 401-407, November 1994.

....Machine Learning, Itemsets, Association Rules. 1 Introduction The problem of mining association rules from market basket data has recently been an important research topic in the area of knowledge discovery from databases. It was originally introduced in [2] and studied extensively in [1, 5, 25, 26, 31, 19, 23, 29, 30, 3, 4, 33, 14]. The problem is typically examined in the context of discovering buying patterns from retail sales transactions. Although there are many similar data mining applications which can be modelled in this way, we again study the problem using the retail store example because of its intuitive nature ....

M. Klemettinen, H. Manilla, P. Ronkainen, H. Toivonen, and A.I. Verkamo. Finding interesting rules from large sets of discovered association rules. In Proceedings of the 3rd International Conference on Information and Knowledge Management, pages 401--407, 1994.

....are discarded. Interestingness of itemsets based on a hierarchy for the items is also discussed in [16] where for a one taxonomy situation a di erent notion of lifting to parents is used. Several other measures of interestingness for the non fuzzy case not involving taxonomies are mentioned in [2, 3, 6, 10, 15] and references in these papers; for a nice overview see [9] We would like to thank Jan Niestadt, Daniel Palomo van Es and the referees for their helpful comments. 2 Fuzzy approach If one considers more general items attributes, one has to deal with non boolean values. Several approaches have ....

M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A.I. Verkamo. Finding interesting rules from large sets of discovered association rules. In Proceedings of the Third International Conference on Information and Knowledge Management (CIKM'94), pages 401-407. ACM Press, 1994.

....the subjective measures those that depend on the class of users who examine the rules [13] Several methods have been proposed to capture interesting rules subjectively.The main idea is to impose users own belief about the domain. Users usually apply their knowledge in terms of rule templates [5] and then try to match the template by scanning the data satisfying some threshold parameters. However specifying the template and later scanning the database to conform with user s belief is not that easy. The problems raised in this approach are : 1. People usually apply their templates in ....

M. Klemettinen and H. Mannila. Finding interesting rules from large sets of discovered association rules. In Proc. Third Int'l Conf. CIKM, pages 401--407, 1994.

....methods. Subjective methods are user driven and domain dependent. For instance, the user may specify rule templates, indicating which combination of attributes must occur in the rule for it to be considered interesting this approach has been used mainly in the context of association rules [40]. As another example of a subjective method, the user can give the system a general, high level description of his her previous knowledge about the domain, so that the system can select only the discovered rules which represent previously unknown knowledge for the user [44] By contrast, ....

Klemettinen M, Mannila H, Ronkainen P, Toivonen H and Verkamo AI. Finding interesting rules from large sets of discovered association rules. Proc. 3rd Int. Conf. on Information and Knowledge Management. Gaithersburg, Maryland. Nov./Dec. 1994.

....these twowords are related thematically, and WordNet does not have thematic connections, an issue which is discussed in detail in Section 6. 5. RELATED WORK Much e ort has gone into reducing mined rule sets by applying both objective and subjective criteria of interestingness. Klemettinen et al. [11] proposed the use of rule templates to describe the structure of relevant rules and constrain the search space. Another notable attempt in using objective measures was byBayardo and Agrawal [1] who de ned a partial order, in terms of both support and con dence, to identify a smaller set of ....

M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. Verkamo. Finding interesting rules from large sets of discovered association rules. In ########### ## ### ##### ############# ############ ########### ### ######### ########## #########, pages 401-407, 1994.

....the pattern Y is more interesting than X as the former has a much larger coverage in the positive data than the latter and both of them have no or almost no occurrence in the negative data. Ranking discovered patterns is an intensively studied topic in data mining, the readers are referred to (Klemettinen et al. 1994; Silberschatz Tuzhilin, 1996; Dong Li, 1998; Padmanabhan Tuzhilin, 1998; Bayardo Agrawal, 1999; Sahar, 1999; Hilderman Hamilton, 2001) for other subjective and objective measurements originated in information theory, statistics, ecology, and economics. 8 Performance Evaluation: ....

Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., & Verkamo, A. (1994). Finding interesting rules from large sets of discovered association rules. Proceedings of the 3rd International Conference on Information and Knowledge Management (pp. 401--408). Gaithersburg, Maryland: ACM Press.

....two separate visualisation techniques. The first can be used to visualise any association rule and the second is specific to temporal associations. The visualisation of sets of association rules has been addressed in a number of different ways. One approach has been to draw connected graphs [6]. However, if the number of rules is large this approach involves a complex layout process that needs to be optimised in order to avoid cluttering the graph. An elegant threedimensional model is provided in the MineSet software tool [4] We have chosen to develop a visualisation that can handle a ....

Klemettinen, M., Mannila H., Ronkainen, P., Toivonen H., Verkamo, A.I. Finding interesting Rules from Large Sets of Discovered Association Rules. Third International Conference on Information and Knowledge Management, Gaithersburg, Maryland, ACM Press. (1994).

....two words are related thematically, and WordNet does not have thematic connections, an issue which is discussed in detail in Section 6. 5. RELATED WORK Much e ort has gone into reducing mined rule sets by applying both objective and subjective criteria of interestingness. Klemettinen et al. [11] proposed the use of rule templates to describe the structure of relevant rules and constrain the search space. Another notable attempt in using objective measures was by Bayardo and Agrawal [1] who de ned a partial order, in terms of both support and con dence, to identify a smaller set of ....

M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. Verkamo. Finding interesting rules from large sets of discovered association rules. In Proceedings of The Third International Conference on Information and Knowledge Management (CIKM-94), pages 401-407, 1994.

..... 3.8 1 smtp 200 300 SF . 5.2 1 http 200 0 REJ . 5.7 2 smtp 300 200 SF . some degree misleading. There is no intuition for the association between the number of bytes from the source, src bytes, and the normal status (i.e. f lag=SF) of the connection. In (Klemettinen et al. 1994), rule templates specifying the allowable attribute values are used to postprocess discovered rules. In (Srikant et al. 1997) boolean expressions over the attribute values are used as item constraints during rule discovery. A drawback of these approaches is that one has to know a priori what ....

....11 12 2000; 14:59; p. 37 38 Lee and Stolfo and Mok Several tools and steps in our framework, for example, using frequent episode programs to find specific patterns, are not fully automatic (e.g. we need to manually inspect the patterns) We need to provide support for rule templates (Klemettinen et al. 1994) so that patterns can be post processed and presented as query results to users. It is important to include users in the knowledge discovery tasks. We are implementing a support environment that integrates the iterative processes of selecting features, and building and evaluating classification ....

Klemettinen, M., H. Mannila, P. Ronkainen, H. Toivonen, and A. I. Verkamo: 1994, `Finding Interesting Rules from Large Sets of Discovered Association Rules'. In: Proceedings of the 3rd International Conference on Information and Knowledge Management (CIKM'94). Gainthersburg, MD, pp. 401--407.

No context found.

Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., Verkamo, A.I.: Finding interesting rules from large sets of discovered association rules. In: Proceedings of the Third International Conference on Information and Knowledge Management (CIKM). (1994) 401--407

No context found.

M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, A.I. Verkamo, Finding interesting rules from large sets of discovered association rules, Proceedings of the Third International Conference on Information and Knowledge Management (CIKM'94), Gaithersburg, MD, ACM, New York, 1994.

....of rules into classes of related rules. While creating a focus, simple threshold like restrictions, such as rule fre quency and con dence may satisfy a large number of rules. In our approach, this problem can be alleviated by selecting rules to or removing rules from the view by templates [32]. Hoschka and Kl osgen [14] have also used templates for de ning interesting knowledge, and their ideas have strongly in uenced our work. Their approach is based on few xed statement types and partial ordering of attributes, whereas our approach is closer to regular expressions. We de ne ....

M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. I. Verkamo, Finding inter - esting rules from large sets of discovered association rules, Proceedings of the Third Interna - tional Conference onInformation andKnowledge Management (CIKM ' 94),ACM, Gaithersburg, Maryland, pp. 401407, November 1994.

No context found.

Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., & Verkamo, A. I. (1995). Finding interesting rules from large sets of discovered association rules. Proc. of CIKM.

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Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., and Verkamo, A. I. 1994. Finding interesting rules from large sets of discovered association rules. In Third International Conference on Information and Knowledge Management (CIKM'94), N. R. Adam, B. K. Bhargava, and Y. Yesha, Eds. ACM Press, 401--407.

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M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. I. Verkamo. Finding interesting rules from large sets of discovered association rules. In N. R. Adam, B. K. Bhargava, and Y. Yesha, editors, Third International Conference on Information and Knowledge Management (CIKM'94), pages 401--407. ACM Press, 1994.

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M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A.I. Verkamo. Finding interesting rules from large sets of discovered association rules. In Intl. Conf. on Information and Knowledge Management (CIKM), November 1994.

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M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen and A.I. Verkamo, Finding interesting rules from large sets of discovered association rules, in: The Third International Conference on Information and Knowledge Management, ACM Press, 1994, pp. 401--407.

No context found.

Mika Klemettinen, Heikki Mannila, Pirjo Ronkainen, Hannu Toivonen, and A. Inkeri Verkamo. Finding interesting rules from large sets of discovered association rules. In ##### ## ### ### #### ##### ## ########### ### ###### #### ########## #########, pages 401 - 407, Gaithersburg, MD, USA, November 1994. ACM.

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M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. I. Verkamo. Finding interesting rules from large sets of discovered association rules. In 3rd Intl. Conf. Information and Knowledge Management, pages 401--407, November 1994. 22

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M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonene, and A. Verkamo. Finding interesting rules from large sets of discovered association rules. In CIKM 94, 1994.

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M. Klemettinen et al. Finding interesting rules from large sets of discovered association rules. In 3rd Intl. Conf. Information and Knowledge Management, pages 401--407, November 1994.

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Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., & Verkamo, A. I. (1995). Finding interesting rules from large sets of discovered association rules. Proc. of CIKM.

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Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., and Verkamo, A. I. (1994). Finding interesting rules from large sets of discovered association rules. In Proc. of the 3rd Int. Conf. on Inform. and Knowl. Management, pages 401--407.

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M. Klemettinen, H. Mannila, P. Ronhainen, H. Toivonen, and A. Verkamo, "Finding Interesting Rules from Large Sets of Discovered Association Rules," Proceedings of the International Conference on Information and Knowledge Management, Gaithersburg, MD, November 1994, pp. 401-408.

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M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A.I. Verkamo. Finding interesting rules from large sets of discovered association rules. In ACM Intl Conf on Information and Knowledge Management (CIKM), pages 401--407, 1994.

No context found.

M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen and A. Verkamo, "Finding Interesting Rules from Large Sets of Discovered Association Rules", Third International Conference on Information and Knowledge Management , Dec 1994.

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M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A.I. Verkamo. Finding interesting rules from large sets of discovered association rules. In Proc. 3rd Int. Conf. Information and Knowledge Management, pp 401-408, 1994.

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M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. I. Verkamo. Finding interesting rules from large sets of discovered association rules. In Proceedings of the Third International Conference on Information and Knowledge Management, December 1994. 30

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M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. I. Verkamo. Finding interesting rules from large sets of discovered association rules. In Nabil R. Adam, Bharat K. Bhargava, and Yelena Yesha, editors, Third International Conference on Information and Knowledge Management (CIKM'94), pages 401 -- 407, Gaithersburg, Maryland, November 1994. ACM Press.

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M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. I. Verkamo. Finding interesting rules from large sets of discovered association rules. Proc. CIKM conf., pp 401-407, November 1994.

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M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. I. Verkamo. Finding interesting rules from large sets of discovered association rules. In 3rd Intl. Conf. Information and Knowledge Management, pages 401-407, November 1994. 37

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M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. I. Verkamo. Finding interesting rules from large sets of discovered association rules. Proc. 3 rd Int. Conf. Information and Knowledge Management (CIKM'94), pages 401-408, Gaithersburg, MD, November 1994.

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M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A.I. Verkamo. Finding interesting rules from large sets of discovered association rules. In N.R. Adam, B.K. Bhargava, and Y. Yesha, editors, ##### ###### ######## ########## ## ########### ### ######### ##########, pages 401-407, Gaithersburg, Maryland, 1994. ACM Press.

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Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H. and Verkamo, A.I., 1994. Finding Interesting Rules from Large Sets of Discovered Association Rules. In Proc. of the Third International Conference on Information and Knowledge Management, pp. 401-407.

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