Rakesh Agrawal, Paul E. Stolorz, Gregory Piatetsky-Shapiro
Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining
KDD, 1998.

@proceedings{KDD-1998,
	address       = "New York City, New York, USA",
	editor        = "Rakesh Agrawal and Paul E. Stolorz and Gregory Piatetsky-Shapiro",
	ee            = "http://www.aaai.org/Library/KDD/kdd98contents.php",
	isbn          = "1-57735-070-7",
	publisher     = "{AAAI Press}",
	title         = "{Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining}",
	year          = 1998,
}

Contents (68 items)

19 ×#mining
11 ×#classification
10 ×#database
10 ×#performance
7 ×#data mining
7 ×#learning
6 ×#algorithm
6 ×#scalability
5 ×#clustering
5 ×#detection

KDD-1998-AlsabtiRS #classification #dataset #named #scalability

CLOUDS: A Decision Tree Classifier for Large Datasets (KA, SR, VS), pp. 2–8.

KDD-1998-BradleyFR #algorithm #clustering #database #scalability

Scaling Clustering Algorithms to Large Databases (PSB, UMF, CR), pp. 9–15.

KDD-1998-DasLMRS

Rule Discovery from Time Series (GD, KIL, HM, GR, PS), pp. 16–22.

KDD-1998-DasMR #similarity

Similarity of Attributes by External Probes (GD, HM, PR), pp. 23–29.

KDD-1998-DehaspeTK

Finding Frequent Substructures in Chemical Compounds (LD, HT, RDK), pp. 30–36.

KDD-1998-Domingos

Occam’s Two Razors: The Sharp and the Blunt (PMD), pp. 37–43.

KDD-1998-EsterFKS #algorithm #database #detection

Algorithms for Characterization and Trend Detection in Spatial Databases (ME, AF, HPK, JS), pp. 44–50.

KDD-1998-GuralnikWS #mining #sequence

Pattern Directed Mining of Sequence Data (VG, DW, JS), pp. 51–57.

KDD-1998-HinneburgK #approach #clustering #database #multi #performance #scalability

An Efficient Approach to Clustering in Large Multimedia Databases with Noise (AH, DAK), pp. 58–65.

KDD-1998-LeeSM #detection #mining #modelling

Mining Audit Data to Build Intrusion Detection Models (WL, SJS, KWM), pp. 66–72.

KDD-1998-LingL #data mining #mining #problem

Data Mining for Direct Marketing: Problems and Solutions (CXL, CL), pp. 73–79.

KDD-1998-LiuHM #classification #mining

Integrating Classification and Association Rule Mining (BL, WH, YM), pp. 80–86.

KDD-1998-NakhaeizadehTL #classification

Evaluating Usefulness for Dynamic Classification (GN, CT, CL), pp. 87–93.

KDD-1998-PadmanabhanT

A Belief-Driven Method for Discovering Unexpected Patterns (BP, AT), pp. 94–100.

KDD-1998-RidgewayMRO #classification #naive bayes

Interpretable Boosted Naïve Bayes Classification (GR, DM, TR, JO), pp. 101–104.

KDD-1998-StaudtKR #data mining #mining

A Data Mining Support Environment and its Application on Insurance Data (MS, JUK, UR), pp. 105–111.

KDD-1998-SteegRW #correlation #detection #higher-order #multi #performance

Coincidence Detection: A Fast Method for Discovering Higher-Order Correlations in Multidimensional Data (EWS, DAR, EW), pp. 112–120.

KDD-1998-WangTL

Interestingness-Based Interval Merger for Numeric Association Rules (KW, SHWT, BL), pp. 121–128.

KDD-1998-AggarwalSY #generative #online

Online Generation of Profile Association Rules (CCA, ZS, PSY), pp. 129–133.

KDD-1998-AndersonM #learning #performance

ADtrees for Fast Counting and for Fast Learning of Association Rules (BSA, AWM), pp. 134–138.

KDD-1998-BerchtoldJR #data mining #diagrams #independence #mining #visual notation

Independence Diagrams: A Technique for Visual Data Mining (SB, HVJ, KAR), pp. 139–143.

KDD-1998-Bhattacharyya #algorithm #modelling #search-based #using

Direct Marketing Response Models Using Genetic Algorithms (SB), pp. 144–148.

KDD-1998-BorgesL #database #hypermedia #mining

Mining Association Rules in Hypertext Databases (JB, ML), pp. 149–153.

KDD-1998-CarlisSK

Blurring the Distinction between Command and Data in Scientific KDD (JVC, ES, SK), pp. 154–158.

KDD-1998-ChanGR #information retrieval #modelling #probability

Probabilistic Modeling for Information Retrieval with Unsupervised Training Data (EPC, SG, SR), pp. 159–163.

KDD-1998-ChanS #case study #detection #learning #scalability #towards

Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection (PKC, SJS), pp. 164–168.

KDD-1998-CohenH #classification #using

Joins that Generalize: Text Classification Using WHIRL (WWC, HH), pp. 169–173.

KDD-1998-CortesP

Giga-Mining (CC, DP), pp. 174–178.

KDD-1998-DebregeasH #interactive

Interactive Interpretation of Kohonen Maps Applied to Curves (AD, GH), pp. 179–183.

KDD-1998-DomshlakGGLMRS #flexibility #framework #named #platform #research

FlexiMine — A Flexible Platform for KDD Research and Application Construction (CD, DG, EG, NL, AM, TR, SES), pp. 184–188.

KDD-1998-DuMouchelS #algorithm #detection #performance #testing

A Fast Computer Intrusion Detection Algorithm Based on Hypothesis Testing of Command Transition Probabilities (WD, MS), pp. 189–193.

KDD-1998-FayyadRB #algorithm #clustering #refinement

Initialization of Iterative Refinement Clustering Algorithms (UMF, CR, PSB), pp. 194–198.

KDD-1998-FeeldersCM #bias #mining

Mining in the Presence of Selectivity Bias and its Application to Reject Inference (AJF, SC, GJM), pp. 199–203.

KDD-1998-GraefeFC #classification #database #on the #performance #scalability #sql #statistics

On the Efficient Gathering of Sufficient Statistics for Classification from Large SQL Databases (GG, UMF, SC), pp. 204–208.

KDD-1998-GrecuB #data mining #distributed #learning #mining

Coactive Learning for Distributed Data Mining (DLG, LAB), pp. 209–213.

KDD-1998-HanGY #database #mining

Mining Segment-Wise Periodic Patterns in Time-Related Databases (JH, WG, YY), pp. 214–218.

KDD-1998-HandleyLR #learning #predict

Learning to Predict the Duration of an Automobile Trip (SH, PL, FAR), pp. 219–223.

KDD-1998-JohnsonKN #2d #performance

Fast Computation of 2-Dimensional Depth Contours (TJ, IK, RTN), pp. 224–228.

KDD-1998-JohnsonD #set

Comparing Massive High-Dimensional Data Sets (TJ, TD), pp. 229–233.

KDD-1998-KellyHA #data mining #mining #performance

Defining the Goals to Optimise Data Mining Performance (MGK, DJH, NMA), pp. 234–238.

KDD-1998-KeoghP #classification #clustering #feedback #performance #representation

An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback (EJK, MJP), pp. 239–243.

KDD-1998-KerberBAS #information management #process

Active Templates: Comprehensive Support for the Knowledge Discovery Process (RK, HB, TA, BS), pp. 244–248.

KDD-1998-KohaviS #classification

Targeting Business Users with Decision Table Classifiers (RK, DS), pp. 249–253.

KDD-1998-KontkanenMST #classification #feature model #named

BAYDA: Software for Bayesian Classification and Feature Selection (PK, PM, TS, HT), pp. 254–258.

KDD-1998-LaneB #concept #identification #learning #online #security

Approaches to Online Learning and Concept Drift for User Identification in Computer Security (TL, CEB), pp. 259–263.

KDD-1998-MacskassyBDH #case study #clustering #performance #web

Human Performance on Clustering Web Pages: A Preliminary Study (SAM, AB, BDD, HH), pp. 264–268.

KDD-1998-McCleanSS #database #information management #nondeterminism

Aggregation of Imprecise and Uncertain Information for Knowledge Discovery in Databases (SIM, BWS, MS), pp. 269–273.

KDD-1998-MegiddoS #predict

Discovering Predictive Association Rules (NM, RS), pp. 274–278.

KDD-1998-MoodyS #learning

Reinforcement Learning for Trading Systems and Portfolios (JEM, MS), pp. 279–283.

KDD-1998-MorzyZ #retrieval

Group Bitmap Index: A Structure for Association Rules Retrieval (TM, MZ), pp. 284–288.

KDD-1998-NakhaeizadehS #algorithm #data mining #evaluation #mining #personalisation #towards

Towards the Personalization of Algorithms Evaluation in Data Mining (GN, AS), pp. 289–293.

KDD-1998-OatesJ #dataset #modelling #scalability

Large Datasets Lead to Overly Complex Models: An Explanation and a Solution (TO, DJ), pp. 294–298.

KDD-1998-OliverRGBBW

Analysing Rock Samples for the Mars Lander (JJO, TR, PG, WLB, RAB, SRW), pp. 299–303.

KDD-1998-ParthasarathyZL #memory management #mining #parallel

Memory Placement Techniques for Parallel Association Mining (SP, MJZ, WL), pp. 304–308.

KDD-1998-PinheiroS #database #mining #semistructured data

Methods for Linking and Mining Massive Heterogeneous Databases (JCP, DXS), pp. 309–313.

KDD-1998-Stolfo #classification #database #mining

Mining Databases with Different Schemas: Integrating Incompatible Classifiers (ALP, SJS), pp. 314–318.

KDD-1998-RaoRC #adaptation #multi

Time Series Forecasting from High-Dimensional Data with Multiple Adaptive Layers (RBR, SR, FC), pp. 319–323.

KDD-1998-Rosset #classification #comparison #evaluation #flexibility #named #performance #ranking

Ranking — Methods for Flexible Evaluation and Efficient Comparison of Classification Performance (SR), pp. 324–328.

KDD-1998-SinghCHSA #architecture #mining #robust

A Robust System Architecture for Mining Semi-Structured Data (LS, BC, RH, PS, KA), pp. 329–333.

KDD-1998-Subramonian #data mining #mining

Defining diff as a Data Mining Primitive (RS), pp. 334–338.

KDD-1998-Suzuki #database #evaluation #reliability

Simultaneous Reliability Evaluation of Generality and Accuracy for Rule Discovery in Databases (ES), pp. 339–343.

KDD-1998-ThomasS #mining #query #sql #using

Mining Generalized Association Rules and Sequential Patterns Using SQL Queries (ST, SS), pp. 344–348.

KDD-1998-WangDB #reduction

Data Reduction Based on Hyper Relations (HW, ID, DAB), pp. 349–353.

KDD-1998-WeigendCFW

Discovering Technical Traders in the T-bond Futures Market (ASW, FC, SF, SRW), pp. 354–358.

KDD-1998-WeissH #learning #predict #sequence

Learning to Predict Rare Events in Event Sequences (GMW, HH), pp. 359–363.

KDD-1998-WuthrichPLCZL #predict

Daily Prediction of Major Stock Indices from Textual WWW Data (BW, DP, SL, VC, JZ, WL), pp. 364–368.

KDD-1998-ZakiLO #mining #named #sequence

PlanMine: Sequence Mining for Plan Failures (MJZ, NL, MO), pp. 369–374.

KDD-1998-GoodmanE #information management #interface #statistics

Knowledge Discovery and the Interface of Computing and Statistics (AG, JFEI), pp. 375–378.