Discovering all most specific sentences by randomized algorithms extended abstract

Gunopulos, Dimitrios; Mannila, Heikki; Saluja, Sanjeev

doi:10.1007/3-540-62222-5_47

Cited by 50 publications

(44 citation statements)

References 10 publications

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“…Bayardo [B98] presented an optimized search method called Max-Miner that prunes out subsets of long patterns of frequent itemsets that are discovered early. Gunopulos et al [GMS97] suggested iteratively extending a working pattern until failure, using a randomized algorithm, which is similar to the idea we used in our algorithm to grow itemsets in a greedy fashion. Much work has been done in automatic clustering methods.…”

Section: Related Workmentioning

confidence: 99%

Efficient discovery of error-tolerant frequent itemsets in high dimensions

Yang

Fayyad

Bradley

2001

Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

113

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We present a generalization of frequent itemsets allowing for the notion of errors in the itemset definition. We motivate the problem and present an efficient algorithm that identifies errortolerant frequent clusters of items in transactional data (customerpurchase data, web browsing data, text, etc.). The algorithm exploits sparseness of the underlying data to find large groups of items that are correlated over database records (rows). The notion of transaction coverage allows us to extend the algorithm and view it as a fast clustering algorithm for discovering segments of similar transactions in binary sparse data. We evaluate the new algorithm on three real-world applications: clustering highdimensional data, query selectivity estimation and collaborative filtering. Results show that the algorithm consistently uncovers structure in large sparse databases that other traditional clustering algorithms fail to find. KeywordsError-tolerant frequent itemset, clustering, high dimensions, query selectivity estimation, collaborative filtering.

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Section: Related Workmentioning

confidence: 99%

Efficient discovery of error-tolerant frequent itemsets in high dimensions

Yang

Fayyad

Bradley

2001

Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

113

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“…In [17] and [26] approaches using only general purpose DBMS systems and relational algebra operations are studied in the context of discovery of association rules. Pincer -Search [18] and All MFS [28] are two algorithms for discovering maximal frequent itemsets. All MFS uses randomized approach while in the PincerSearch a combination of the bottom up approach like the a priori method and a top down search with a view to reduce the database scans is employed.…”

Section: Related Workmentioning

confidence: 99%

Reduction of Number of Association Rules with Inter Itemset Distance in Transaction Databases

Kumar¹,

Sarma²,

Mahanta³

2012

IJDMS

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Association Rule discovery has been an important problem of investigation in knowledge discovery and data mining. An association rule describes associations among the sets of items which occur together in transactions of databases.The Association Rule mining task consists of finding the frequent itemsets and the rules in the form of conditional implications with respect to some prespecified threshold values of support and confidence.The interestingness of Association

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“…The number of valid packages to go through can be overwhelming when the number of satellite items is large, which is typically the case. As a result, we propose an alternative algorithm (adapted from [7]), MaxCompositeItemSet, that computes an approximate Mc based on random walks.…”

Section: Maximal Package Constructionmentioning

confidence: 99%

Constructing and exploring composite items

Roy

Amer-Yahia

Chawla

et al. 2010

Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data

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Nowadays, online shopping has become a daily activity. Web users purchase a variety of items ranging from books to electronics. The large supply of online products calls for sophisticated techniques to help users explore available items. We propose to build composite items which associate a central item with a set of packages, formed by satellite items, and help users explore them. For example, a user shopping for an iPhone (i.e., the central item) with a price budget can be presented with both the iPhone and a package of other items that match well with the iPhone (e.g., {Belkin case, Bose sounddock, Kroo USB cable}) as a composite item, whose total price is within the user's budget. We define and study the problem of effective construction and exploration of large sets of packages associated with a central item, and design and implement efficient algorithms for solving the problem in two stages: summarization, a technique which picks k representative packages for each central item; and visual effect optimization, which helps the user find diverse composite items quickly by minimizing overlap between packages presented to the user in a ranked order. We conduct an extensive set of experiments on Yahoo! Shopping 1 data sets to demonstrate the efficiency and effectiveness of our algorithms.

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Discovering all most specific sentences by randomized algorithms extended abstract

Cited by 50 publications

References 10 publications

Efficient discovery of error-tolerant frequent itemsets in high dimensions

Efficient discovery of error-tolerant frequent itemsets in high dimensions

Reduction of Number of Association Rules with Inter Itemset Distance in Transaction Databases

Constructing and exploring composite items

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