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Abstract: . One of the defining challenges for the KDD research community is to enable inductive learning algorithms to mine very large databases. This paper summarizes, categorizes, and compares existing work on scaling up inductive algorithms. We concentrate on algorithms that build decision trees and rule sets, in order to provide focus and specific details; the issues and techniques generalize to other types of data mining. We begin with a discussion of important issues related to scaling up. We... (Update)
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BibTeX entry: (Update)
Provost, F., & Kolluri, V. (1999). A survey of methods for scaling up inductive algorithms. http://citeseer.ist.psu.edu/provost99survey.html More
@article{ provost99survey,
author = "Foster J. Provost and Venkateswarlu Kolluri",
title = "A Survey of Methods for Scaling Up Inductive Algorithms",
journal = "Data Mining and Knowledge Discovery",
volume = "3",
number = "2",
pages = "131-169",
year = "1999",
url = "citeseer.ist.psu.edu/provost99survey.html" }
Citations (may not include all citations):2133 Pattern Classification and Scene Analysis (context) - Duda, Hart - 1973
1359 Induction of decision trees (context) - Quinlan - 1986 ACM DBLP
1262 Classification and Regression Trees (context) - Breiman, Friedman et al. - 1984
910 Fast algorithms for mining association rules - Agrawal, Srikant - 1994
667 UCI repository of machine learning databases (context) - Merz, Murphy - 1997
388 Inductive Logic Programming - Muggleton - 1992 ACM DBLP
342 Wrappers for feature subset selection - Kohavi, John - 1997 ACM DBLP
317 Learning quickly when irrelevant attributes abound: A new li.. (context) - Littlestone - 1988
274 Generalization as search (context) - Mitchell - 1982 DBLP
262 From data mining to knowledge discovery: An overview (context) - Fayyad, Piatetsky-Shapiro et al. DBLP
248 Fast effective rule induction - Cohen - 1995 DBLP
216 Very simple classification rules perform well on most common.. (context) - Holte - 1993 ACM DBLP
209 Mining quantitative association rules in large relational ta.. - Srikant, Agrawal - 1996 ACM DBLP
189 Sampling large databases for association rules - Toivonen - 1996
164 An efficient algorithm for mining association rules in large.. (context) - Savasere, Omiecinski et al. - 1995 ACM DBLP
163 Learning efficient classification procedures and their appli.. (context) - Quinlan - 1983
162 Simplifying decision trees - Quinlan - 1987 ACM DBLP
147 Boolean feature discovery in empirical learning (context) - Pagallo, Haussler - 1990 ACM DBLP
145 SPRINT: A scalable parallel classifier for data mining - Shafer, Agrawal et al. - 1996 DBLP
131 Data mining: An overview from database perspective - Chen, Han et al. - 1997
111 SLIQ: A fast scalable classifier for data mining - Mehta, Agrawal et al. - 1996 DBLP
107 Efficient noise-tolerant learning from statistical queries - Kearns - 1993 ACM DBLP
99 Concept learning and the problem of small disjuncts (context) - Holte, Acker et al. - 1989
96 The need for biases in learning generalizations - Mitchell - 1980
95 Estimating attributes: Analysis and extensions of Relief - Kononenko - 1994 DBLP
87 Subset selection in regression (context) - Miller - 1990
86 JAM: Java agents for meta-learning over distributed database.. - Stolfo, Prodromidis et al. - 1997 DBLP
83 Incremental induction of decision trees - Utgoff - 1989 ACM DBLP
79 Error reduction through learning multiple descriptions - Ali, Pazzani - 1996 ACM DBLP
62 Megainduction: Machine learning on very large databases (context) - Catlett
60 A theory of learning classification rules - Buntine - 1991
59 Toward parallel and distributed learning by meta-learning - Chan, Stolfo - 1993
57 DBMiner: A system for mining knowledge in large relational d.. - Han, Fu et al. - 1996 DBLP
55 Integrating association rule mining with relational database.. - Sarawagi, Thomas et al. - 1998 DBLP
53 Knowledge discovery and data mining: Towards a unifying fram.. - Fayyad, Piatetsky-Shapiro et al. DBLP
51 Wrappers for Performance Enhancement and Oblivious Decision .. - Kohavi - 1996 ACM
50 An experimental comparison of symbolic and connectionist lea.. (context) - Shavlik, Mooney et al. - 1991 DBLP
49 Adaptive fraud detection - Fawcett, Provost - 1997 ACM DBLP
47 Parallel depth-first search (context) - Kumar, Rao - 1987
47 Theory and applications of agnostic pac-learning with small .. - Auer, Holte et al. - 1995 DBLP
47 Megainduction: A test flight (context) - Catlett
45 An experimental comparison of the nearestneighbor and neares.. - Wettschereck, Dietterich - 1995
42 Mining Very Large Databases with Parallel Processing (context) - Freitas, Lavington - 1997 ACM
42 An information theoretic approach to rule induction from dat.. (context) - Smyth, Goodman - 1992 ACM DBLP
42 Efficiently inducing determinations: A complete and systemat.. - Schlimmer - 1993 DBLP
42 The logic of frames (context) - Hayes - 1979
40 Extracting Comprehensible Models from Trained Neural Network.. (context) - Craven - 1996 ACM
37 the accuracy of meta-learning for scalable data mining - Chan, Stolfo - 1997
34 Developing tightly-coupled data mining applications on a rel.. - Agrawal, Shim - 1996 DBLP
33 Incremental reduced error pruning (context) - Furnkranz, Widmer - 1994 DBLP
32 Static versus dynamic sampling for data mining - John, Langley - 1996 DBLP
32 Small disjuncts in action: Learning to diagnose errors in th.. (context) - Danyluk, Provost - 1993
31 An SE-tree based characterization of the induction problem - Rymon - 1993 DBLP
30 Representation design and brute-force induction in a boeing .. - Riddle, Segal et al. - 1994
30 Knowledge acquisition from examples via multiple models - Domingos - 1997
29 Credit card fraud detection using meta-learning: Issues and .. - Stolfo, Fan et al. - 1997
28 Data Surveyor: Searching the nuggets in parallel (context) - Holsheimer, Kersten et al. - 1996 DBLP
28 Decision theoretic subsampling for induction on large databa.. (context) - Musick, Catlett et al. - 1993 DBLP
28 Inductive policy: The pragmatics of bias selection (context) - Provost, Buchanan - 1995 DBLP
28 Efficient pruning methods for separate-and-conquer rule lear.. (context) - Cohen - 1993 DBLP
27 Scaling up: Distributed machine learning with cooperation - Provost, Hennessy - 1996 DBLP
27 Evaluation of sampling for data mining of association rules - Zaki, Parthasarathy et al. - 1997 ACM DBLP
25 Feature subset selection using the wrapper model: Overfittin.. (context) - Kohavi, Sommerfield - 1995
24 Learning decision lists using homogeneous rules - Segal, Etzioni ACM DBLP
23 A review and comparative evaluation of feature weighting met.. (context) - Wettschereck, Aha et al. - 1997
23 The effects of training set size on decision tree complexity - Oates, Jensen - 1997 ACM DBLP
23 Scaling up inductive learning with massive parallelism - Provost, Aronis - 1996 ACM DBLP
22 Induction of one-level decision trees - Iba, Langley - 1992
22 KDD for science data analysis: Issues and examples - Fayyad, Haussler et al. - 1996 DBLP
22 Scaling up inductive logic programming by learning from inte.. - Blockeel, De Raedt et al. - 1999 DBLP
22 Large data sets lead to overly complex models: an explanatio.. - Oates, Jensen - 1998
22 An ounce of knowledge is worth a ton of data: Quantitative s.. - Gaines - 1989
21 Maximizing the predictive value of production rules (context) - Weiss, Galen et al. - 1990 ACM DBLP
21 Multiple comparisons in induction algorithms - Jensen, Cohen - 1999 ACM DBLP
20 RL4: A tool for knowledge-based induction (context) - Clearwater, Provost - 1990
20 SKICAT: A machine learning system for automated cataloging o.. (context) - Fayyad, Weir et al. - 1993
19 Knowledge discovery from multiple databases (context) - Ribeiro, Kaufmann et al. - 1995 DBLP
18 Efficient progressive sampling - Provost, Jensen et al. - 1999 ACM DBLP
18 Distributed machine learning: scaling up with coarsegrained .. (context) - Provost, Hennessy - 1994
18 Communications of the ACM (context) - Fox, Akscyn et al. - 1995
17 An overview of issues in developing industrial data mining a.. (context) - Piatetsky-Shapiro, Brachman et al. - 1996 DBLP
17 Pruning meta-classifiers in a distributed data mining system - Prodromidis, Stolfo - 1998
15 Incremental batch learning (context) - Clearwater, Cheng et al. - 1989 ACM DBLP
15 Overcoming the myopia of inductive learning algorithms with .. - Kononenko, Simec et al. - 1997
15 The WoRLD: Knowledge discovery from multiple distributed dat.. - Aronis, Kolluri et al. - 1997
14 DENDRAL and META-DENDRAL: Their applications dimensions (context) - Buchanan, Feigenbaum - 1978
14 Problem solving and rule induction: A unified view (context) - Simon, Lea - 1973
13 Exploiting background knowledge in automated discovery - Aronis, Provost et al. - 1996 DBLP
13 Efficiently constructing relational features from background.. - Aronis, Provost - 1994 DBLP
13 Linear time rule induction - Domingos
12 Knowledge representation in the large (context) - Karp, Paley - 1995 DBLP
10 Scaling Up inductive algorithms: An overview - Provost, Kolluri DBLP
10 PARKA: A System for Massively Parallel Knowledge Representat.. (context) - Evett - 1994
9 Developing tightly-coupled applications on IBM DB2/CS relati.. (context) - Agrawal, Shim - 1995
9 Knowledge probing in distributed data mining (context) - Guo, Sutiwaraphun - 1998
9 SIPping from the data firehose - John, Lent - 1997 DBLP
9 Scalable Data Mining for Rules (context) - Zaki - 1998 ACM
9 Data Mining and Knowledge Discovery (context) - Fayyad - 1997 ACM
9 Massively parallel matching of knowledge structures - Andersen, Hendler et al. - 1994 ACM
8 Accelerated learning on the connection machine - Cook, Holder - 1990
8 Using SQL primitives and parallel DB servers to speed up kno.. - Freitas, Lavington - 1996
8 Efficient specific-to-general rule induction (context) - Domingos DBLP
7 A method for reasoning with structured and continuous attrib.. - Kaufman, Michalski - 1996
7 Scalable parallel classification for data mining on shared m.. (context) - Zaki, Ho et al. - 1999
7 Applications of artificial intelligence for chemical inferen.. (context) - Buchanan, Smith et al. - 1976 ACM
6 the efficient gathering of sufficient statistics for classif.. - Graefe, Fayyad et al. - 1998
6 Policies for the Selection of Bias in Inductive Machine Lear.. (context) - Provost - 1992 ACM
6 Modeling decision tree performance with the power law (context) - Frey, Fisher - 1999
6 Increasing the efficiency of data mining algorithms with bre.. - Aronis, Provost - 1997
6 Special issue on bias evaluation and selection (context) - DesJardins, Gordon - 1995
5 Inducing and Combining Multiple Decision Trees (context) - Williams - 1990
5 Multi-layer incremental induction - Wu, Lo - 1998 ACM DBLP
5 Private communication (context) - Jensen - 1998
4 Direct access of an ILP algorithm to a database management s.. - Brockhausen, Morik - 1996
4 Using multi-attribute predicates for mining classification r.. (context) - Chen - 1995 ACM DBLP
4 A storage system for scalable knowledge representation (context) - Karp, Paley et al. - 1994 ACM DBLP
4 ARIEL: A massively parallel symbolic learning assistant for .. (context) - Lathrop, Webster et al. - 1990
4 Generating C4 (context) - Kufrin - 1997
4 Sample size and misclassification: Is more always better (context) - Harris-Jones, Haines - 1997
3 A comparison of prediction accuracy (context) - Lim, Loh - 1999
3 Data mining and statistics: What's the connection (context) - Friedman - 1997
3 Integrative windowing - Furnkranz - 1998 DBLP
3 On handling tree-structure attributes in decision tree learn.. (context) - Almuallim, Akiba et al. - 1995
3 Private communication (context) - Haines - 1998
3 Learning decision lists using homogenous rules (context) - Segal, Etzioni
2 A survey of methods for scaling up inductive learning (context) - Provost, Kolluri
2 A tutorial introduction to high performance data mining (context) - Grossman, Bailey - 1998
2 Rainforest - a frameword for fast decision tree construction.. (context) - Gehrke, Ramakrishnan et al. - 1998
2 Exploiting parallelism in a scientific discovery system to i.. - Galal, Cook et al. - 1999
2 Free Parallel Data Mining - Li - 1998 ACM DBLP
2 Quantifiying inductive bias: AI learning algorithms and Vali.. (context) - Haussler - 1988
2 Combining decision trees learned in parallel - Hall, Chawla et al. - 1998
1 Crossing the chasm: From academic machine learning to commer.. (context) - Kohavi - 1998
1 KDD-98 Workshop on Distributed Data Mining (context) - Kargupta, Chan - 1998
1 From large to huge: A statistician's reaction to KDD and DM (context) - INDUCTIVE, Huber - 1997
1 Supporting large-scale computational science (context) - Musick - 1998
1 Towards scalable learning with non-uniform class and cost di.. (context) - INDUCTIVE, Chan et al. - 1998
1 Iterative weakening: Optimal and near-optimal policies for t.. (context) - INDUCTIVE, Provost - 1993 DBLP
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