KDD-Cup 2000 organizers' report: peeling the onion

By Ron Kohavi, Carla E. Brodley, Brian Frasca, Llew Mason, Zijian Zheng. SKDD, 2000

303 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/98abcb842709f2192b0d1020810ff0a0b5901b47

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Jiawei Han
2. George Karypis
3. Mukund Deshpande
4. Pedro M. Domingos
5. Daniel Lowd
6. Jian Pei
7. Jianyong Wang
8. Johannes Gehrke

In Strategy and Management

1. Bart Goethals
2. Christian Borgelt

Citations in the corpus

• Selective Markov models for predicting Web page accesses (2004, methodology): “com, and was used as part of the KDDCup 2000 competition [Kohavi et al. 2000].”
• Krimp: mining itemsets that compress (2011, methodology): “From the FIMI repository (Goethals and Zaki 2003) we use the BMS datasets2 (Kohavi et al. 2000).”
• Decision trees for mining data streams (2006, unknown): “We use the aggregated data set of question 1 [21].”
• Are your Items in Order (2019, unknown): “gz data [12].”
• MAFIA: a maximal frequent itemset algorithm (2005, methodology): “The second data set type is click stream data from two different e-commerce Web sites (BMS-WebView-1 and BMS- WebView-2) where each transaction is a Web session and each item is a product page view; this data was provided by Blue Martini [36].”
• Mining sequential patterns by pattern-growth: the PrefixSpan approach (2004, background): “More detailed information about this data set can be found in [10].”
• Survey on Frequent Pattern Mining (2003, methodology): “The BMS-WebView-1 data set contains several months worth of clickstream data from an e-commerce web site, and is made publicly available by Blue Martini Software [23].”
• LCM ver. 2: Efficient Mining Algorithms for Frequent/Closed/Maximal Itemsets (2004, unknown): “これらの問 題に対しては,非常に多くのアルゴリズムが提案されている [1, 2, 3, 8, 9, 14, 18, 17, 20, 21].”
• EFFICIENT IMPLEMENTATIONS OF APRIORI AND ECLAT (2003, methodology): “The data sets I used are: BMS-Webview-1 (a web click stream from a leg-care company that no longer exists, which has been used in the KDD cup 2000 [ 7 , 9]), T10I4D100K (an artificial data set generated with IBM’s data generator [10]), census (a data set derived from an extract of the US census bure”
• Naive Bayes models for probability estimation (2005, methodology): “Because of this, any exact inference algorithms will only be tractable on small or specially structured Bayesian networks.”