Doheon Lee, Mario Schkolnick, Foster J. Provost, Ramakrishnan Srikant
Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining
KDD, 2001.

@proceedings{KDD-2001,
	acmid         = "502512",
	address       = "San Francisco, California, USA",
	editor        = "Doheon Lee and Mario Schkolnick and Foster J. Provost and Ramakrishnan Srikant",
	isbn          = "1-58113-391-X",
	publisher     = "{ACM}",
	title         = "{Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining}",
	year          = 2001,
}

Contents (72 items)

24 ×#mining
9 ×#data mining
7 ×#modelling
7 ×#scalability
7 ×#using
7 ×#web
6 ×#algorithm
6 ×#classification
6 ×#clustering
6 ×#named

KDD-2001-Altman #biology #challenge #information management

Challenges for knowledge discovery in biology (RBA), p. 2.

KDD-2001-Mitchell #web

Extracting targeted data from the web (TMM), p. 3.

KDD-2001-Ramakrishnan #collaboration #data mining #mining

Mass collaboration and data mining (RR), p. 4.

KDD-2001-Agrarwal #modelling #predict

Applications of generalized support vector machines to predictive modeling (NA), p. 6.

KDD-2001-Edelstein #data mining #mining #question

Data mining: are we there yet? (HE), p. 7.

KDD-2001-Kohavi #e-commerce #mining

Mining e-commerce data: the good, the bad, and the ugly (RK), pp. 8–13.

KDD-2001-Netz #data mining #database #developer #framework #mining #platform

Data mining platform for database developers (AN), p. 14.

KDD-2001-Riedl #community #recommendation

Recommender systems in commerce and community (JR), p. 15.

KDD-2001-BiFK #mining

The “DGX” distribution for mining massive, skewed data (ZB, CF, FK), pp. 17–26.

KDD-2001-BujaL #classification #data mining #mining

Data mining criteria for tree-based regression and classification (AB, YSL), pp. 27–36.

KDD-2001-CadezSM #modelling #predict #probability #profiling #transaction #visualisation

Probabilistic modeling of transaction data with applications to profiling, visualization, and prediction (IVC, PS, HM), pp. 37–46.

KDD-2001-DittrichS #algorithm #mining #named #scalability #set

GESS: a scalable similarity-join algorithm for mining large data sets in high dimensional spaces (JPD, BS), pp. 47–56.

KDD-2001-DomingosR #mining #network

Mining the network value of customers (PMD, MR), pp. 57–66.

KDD-2001-DuMouchelP #empirical #multi

Empirical bayes screening for multi-item associations (WD, DP), pp. 67–76.

KDD-2001-FungM #classification

Proximal support vector machine classifiers (GF, OLM), pp. 77–86.

KDD-2001-GarckeG #data mining #mining #using

Data mining with sparse grids using simplicial basis functions (JG, MG), pp. 87–96.

KDD-2001-HultenSD #data type #mining

Mining time-changing data streams (GH, LS, PMD), pp. 97–106.

KDD-2001-Kandogan #clustering #coordination #multi #roadmap #using #visualisation

Visualizing multi-dimensional clusters, trends, and outliers using star coordinates (EK), pp. 107–116.

KDD-2001-KeoghCP #approach #database #named #scalability

Ensemble-index: a new approach to indexing large databases (EJK, SC, MJP), pp. 117–125.

KDD-2001-KnorrNZ #robust

Robust space transformations for distance-based operations (EMK, RTN, RHZ), pp. 126–135.

KDD-2001-KramerRH #mining

Molecular feature mining in HIV data (SK, LDR, CH), pp. 136–143.

KDD-2001-LiuMY #web

Discovering unexpected information from your competitors’ web sites (BL, YM, PSY), pp. 144–153.

KDD-2001-PadmanabhanZK #personalisation #semistructured data #what

Personalization from incomplete data: what you don’t know can hurt (BP, Z(Z, SOK), pp. 154–163.

KDD-2001-PavlovS #modelling #probability #query #transaction

Probabilistic query models for transaction data (DP, PS), pp. 164–173.

KDD-2001-PennockLNG #game studies #probability #web

Extracting collective probabilistic forecasts from web games (DMP, SL, FÅN, CLG), pp. 174–183.

KDD-2001-TrainaTPF #data mining #mining #multi #named #scalability #tool support

Tri-plots: scalable tools for multidimensional data mining (AJMT, CTJ, SP, CF), pp. 184–193.

KDD-2001-YangFB #performance

Efficient discovery of error-tolerant frequent itemsets in high dimensions (CY, UMF, PSB), pp. 194–203.

KDD-2001-ZadroznyE #learning

Learning and making decisions when costs and probabilities are both unknown (BZ, CE), pp. 204–213.

KDD-2001-AdderleyM #behaviour #case study #commit #data mining #mining #modelling

Data mining case study: modeling the behavior of offenders who commit serious sexual assaults (RA, PBM), pp. 215–220.

KDD-2001-Aggarwal #clustering #effectiveness #human-computer

A human-computer cooperative system for effective high dimensional clustering (CCA), pp. 221–226.

KDD-2001-AggarwalP #concept #mining #re-engineering #semistructured data #set

Mining massively incomplete data sets by conceptual reconstruction (CCA, SP), pp. 227–232.

KDD-2001-BasuMPG #using

Evaluating the novelty of text-mined rules using lexical knowledge (SB, RJM, KVP, JG), pp. 233–238.

KDD-2001-BeygelzimerPM #category theory #dataset #performance #scalability #visualisation

Fast ordering of large categorical datasets for better visualization (AB, CSP, SM), pp. 239–244.

KDD-2001-BinghamM #image #random #reduction

Random projection in dimensionality reduction: applications to image and text data (EB, HM), pp. 245–250.

KDD-2001-CarageaCH #classification #using

Gaining insights into support vector machine pattern classifiers using projection-based tour methods (DC, DC, VH), pp. 251–256.

KDD-2001-ChenCS #adaptation #named #self

PVA: a self-adaptive personal view agent system (CCC, MCC, YSS), pp. 257–262.

KDD-2001-ChiuFCWJ #algorithm #clustering #database #robust #scalability

A robust and scalable clustering algorithm for mixed type attributes in large database environment (TC, DF, JC, YW, CJ), pp. 263–268.

KDD-2001-Dhillon #clustering #documentation #graph #using #word

Co-clustering documents and words using bipartite spectral graph partitioning (ISD), pp. 269–274.

KDD-2001-DingHZ #component #graph #web

A spectral method to separate disconnected and nearly-disconnected web graph components (CHQD, XH, HZ), pp. 275–280.

KDD-2001-HarelK #clustering #random #using

Clustering spatial data using random walks (DH, YK), pp. 281–286.

KDD-2001-IndurkhyaW #classification #problem #rule-based

Solving regression problems with rule-based ensemble classifiers (NI, SMW), pp. 287–292.

KDD-2001-JinTH #database #mining #scalability

Mining top-n local outliers in large databases (WJ, AKHT, JH), pp. 293–298.

KDD-2001-KaltonLWY #clustering #learning

Generalized clustering, supervised learning, and data assignment (AK, PL, KW, JPY), pp. 299–304.

KDD-2001-LambertP #mining #transaction

Mining a stream of transactions for customer patterns (DL, JCP), pp. 305–310.

KDD-2001-LazarevicO #algorithm #distributed

The distributed boosting algorithm (AL, ZO), pp. 311–316.

KDD-2001-LinP #induction #natural language #semantics

Induction of semantic classes from natural language text (DL, PP), pp. 317–322.

KDD-2001-LinP01a

DIRT @SBT@discovery of inference rules from text (DL, PP), pp. 323–328.

KDD-2001-LiuHM #identification

Identifying non-actionable association rules (BL, WH, YM), pp. 329–334.

KDD-2001-LiuHM01a #set

Discovering the set of fundamental rule changes (BL, WH, YM), pp. 335–340.

KDD-2001-MannilaS #sequence

Finding simple intensity descriptions from event sequence data (HM, MS), pp. 341–346.

KDD-2001-MoodySV #automation #classification

Data filtering for automatic classification of rocks from reflectance spectra (JM, RBdAeS, JV), pp. 347–352.

KDD-2001-Morimoto #database #mining #set

Mining frequent neighboring class sets in spatial databases (YM), pp. 353–358.

KDD-2001-OzaR #online

Experimental comparisons of online and batch versions of bagging and boosting (NCO, SJR), pp. 359–364.

KDD-2001-SevonTO #named

TreeDT: gene mapping by tree disequilibrium test (PS, HT, VO), pp. 365–370.

KDD-2001-ShekharLZ #algorithm #detection #graph #summary

Detecting graph-based spatial outliers: algorithms and applications (a summary of results) (SS, CTL, PZ), pp. 371–376.

KDD-2001-StreetK #algorithm #classification #scalability #streaming

A streaming ensemble algorithm (SEA) for large-scale classification (WNS, YK), pp. 377–382.

KDD-2001-Webb

Discovering associations with numeric variables (GIW), pp. 383–388.

KDD-2001-YamanishiT

Discovering outlier filtering rules from unlabeled data: combining a supervised learner with an unsupervised learner (KY, JiT), pp. 389–394.

KDD-2001-YangWY #mining #named

Infominer: mining surprising periodic patterns (JY, WW, PSY), pp. 395–400.

KDD-2001-ZhengKM #algorithm #performance

Real world performance of association rule algorithms (ZZ, RK, LM), pp. 401–406.

KDD-2001-ApteBNPTCN #modelling

Segmentation-based modeling for advanced targeted marketing (CA, EB, RN, EPDP, FT, DC, BN), pp. 408–413.

KDD-2001-BerkhinBR #analysis #interactive #web

Interactive path analysis of web site traffic (PB, JDB, DJR), pp. 414–419.

KDD-2001-DattaDBMH

Estimating business targets (PD, JD, AB, DRM, JH), pp. 420–425.

KDD-2001-Elkan #challenge #data mining #lessons learnt #mining

Magical thinking in data mining: lessons from CoIL challenge 2000 (CE), pp. 426–431.

KDD-2001-HotzGHNW #analysis #detection #industrial

REVI-MINER, a KDD-environment for deviation detection and analysis of warranty and goodwill cost statements in automotive industry (EH, UG, WH, GN, MW), pp. 432–437.

KDD-2001-HueglinV #data mining #mining

Data mining techniques to improve forecast accuracy in airline business (CH, FV), pp. 438–442.

KDD-2001-LiY #mining

Mining from open answers in questionnaire data (HL, KY), pp. 443–449.

KDD-2001-MahHL #mining #network

Funnel report mining for the MSN network (TM, HH, YL), pp. 450–455.

KDD-2001-RossetNEVI #evaluation #modelling #predict

Evaluation of prediction models for marketing campaigns (SR, EN, UE, NV, YI), pp. 456–461.

KDD-2001-SpanglerK #analysis #knowledge base #maintenance #using

Knowledge base maintenance using knowledge gap analysis (WSS, JTK), pp. 462–466.

KDD-2001-WarnerRDB #interactive #knowledge base #mining #web

Mining user session data to facilitate user interaction with a customer service knowledge base in RightNow Web (DW, JNR, SDD, BB), pp. 467–472.

KDD-2001-YangZL #mining #modelling #predict #web

Mining web logs for prediction models in WWW caching and prefetching (QY, HHZ, ITYL), pp. 473–478.