Set-oriented mining for association rules in relational databases

Houtsma, Maurice A. W.; Swami, Arun

doi:10.1109/icde.1995.380413

Cited by 211 publications

(144 citation statements)

References 8 publications

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“…Many algorithms have been proposed to solve this problem, from the original proposals, such as AIS (Agrawal, Imielinski, & Swami, 1993), SETM (Houtsma & Swami, 1993), and, in particular, Apriori (Agrawal & Srikant, 1994), to more advanced algorithms such as DHP (Park, Chen, & Yu, 1995), DIC (Brin et al, 1997), CARMA (Hidber, 1999), FP-Growth (Han, Pei, & Yin, 2000), and TBAR (Berzal et al, 2001). See Hipp, Güntzer, and Nakhaeizadeh (2000) for a recent survey of the problem and Han and Plank (1996) for a somewhat older comparison of some selected algorithms.…”

Section: Association Rulesmentioning

confidence: 99%

ART: A Hybrid Classification Model

et al. 2004

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Abstract. This paper presents a new family of decision list induction algorithms based on ideas from the association rule mining context. ART, which stands for 'Association Rule Tree', builds decision lists that can be viewed as degenerate, polythetic decision trees. Our method is a generalized "Separate and Conquer" algorithm suitable for Data Mining applications because it makes use of efficient and scalable association rule mining techniques.

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Section: Association Rulesmentioning

confidence: 99%

ART: A Hybrid Classification Model

et al. 2004

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“…One disadvantage of the AIS algorithm is that it generates too many invalid candidate itemsets. Houtsma and Swami proposed the SETM algorithm [4] that uses SQL for generating the frequent itemsets. Although it uses standard SQL join operation for generating candidate itemsets, the SETM algorithm generates candidate itemsets through a process of iterations similar to that of the AIS algorithm.…”

Section: Related Workmentioning

confidence: 99%

Mining Association Rules Based on Boolean Algorithm - a Study in Large Databases

Rao¹,

Babu²,

Shankar³

et al. 2013

IJMLC

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Abstract-With the wide applications of computers and automated data gathering tools, massive amounts of data have constantly collected in databases, which create immense demand for analyzing data and turning them into useful knowledge. Therefore, Knowledge Discovery and Data mining has become a research field in recent years to analyze the data in large databases. Association rule mining is one of the dominant methods for market basket analysis, which analyzes customer buying habits.The problem of association rule mining is that there are so many promising rules; it is obvious that such a vast amount of rules cannot be processed by inspecting each one. Therefore efficient algorithms restrict the search space and check only a subset of all rules. Boolean algorithm is one technique for mining association rules. The first objective of this study is to generate Association rules from massive databases in order to entrepreneurs can enlarge their own marketing strategies. The second objective of this study is to implement the program in the most efficient way in order to decrease the processing time and the memory consumption. This study can help retailers to build marketing strategies by gaining information about which items are frequently purchased together by customers.

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“…2. Association Rules (Agrawal et al 1993;Houtsma, Swami 1995): focusing on two items (or more) of information and searching for connections between them. 3.…”

Section: Dm Analysismentioning

confidence: 99%

Utilizing Data Mining to Discover Knowledge in Construction Enterprise Performance Records/Utilizuotas Duomenų Kaupimas

Lee

Hsueh

Tseng

2008

Journal of Civil Engineering and Management

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Abstract. Although Data Mining (DM) has been applied extensively in information systems and identified as a crucial tool for automatic data analysis and enterprise knowledge inference, still the practicability of DM has been little explored in the construction industry in particular. This study is conducted using data from actual practice in the customer service department of the target enterprise. Starting with data preparation, decision tree analysis and domain knowledge inference, practical verification is performed and previously unknown knowledge is discovered. Two practical barriers to enterprise information mining are found: the separation of information among various information systems and lack of key information attributes. Hence, there is a significant limitation on practical information data mining to generate new information, and only with a proper information integration one can benefit from the potential practical application of DM.

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Set-oriented mining for association rules in relational databases

Cited by 211 publications

References 8 publications

ART: A Hybrid Classification Model

ART: A Hybrid Classification Model

Mining Association Rules Based on Boolean Algorithm - a Study in Large Databases

Utilizing Data Mining to Discover Knowledge in Construction Enterprise Performance Records/Utilizuotas Duomenų Kaupimas

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