資料探勘文獻
期刊與研討會
IEEE Transactions on Knowledge and Data Engineering (TKDE)
Journal of Data Mining and Knowledge Discovery (JDMKD)
Journal of Very Large Database Systems (JVLDS)
Journal of Visual Language and Computing (JVLC)
Journal of Intelligent Information Systems (JIIS)
Journal of Intelligent Data Analysis (JIDA)
Data and Knowledge Engineering (DKE)
Machine Learning (ML)
ACM SIGMOD Record (SIGMODR)
ACM Int’l Conf. on Management of Data (ICMOD)
IEEE Int’l Conf. on Data Engineering (ICDE)
IEEE Int’l Conf. on Information Visualization (ICIV)
Int’l Conf. on Knowledge Discovery and Data Mining (ICKDD)
Int’l Conf. on Very Large Databases (ICVLDB)
Int’l Conf. on Information and Knowledge Management (CIKM)
Int’l Symp. on Methodologies for Intelligent Systems (ISMIS)
Conference on Machine Learning
網路資源
data mining
group has much resources
Ÿ
The collection of Computer
Science bibliographies
Lewis, D.,
1997. The reuters-21578, distribution 1.0
Ÿ
http://www.ulb.ac.be/di/bookmarks/book.html#cs
概論
Fayyad, U., “From data mining to knowledge discovery: an overview”,
Advances in KDD
Brachman, R., “Mining business databases”, Communication of ACM,
Nov. 1996
Simoudis, E., “Reality check for data mining”, IEEE EXPERT, Oct.
1996
Fayyad, U., “Knowledge discovery and data mining: towards a unifying
framework”, KDD96
Piatetsky-Shapiro, G., “An overview of issues in developing
industrial data mining and knowledge discovery applications”, ICKDD 96
Mitchell, T., Machine Learning, McGraw-Hill, 1997
資料探勘應用
John, G., “Stock selection using rule induction”, IEEE EXPERT, Oct.
1996
Dao, S.“Applying a data miner to heterogeneous schema integration”,
KDD95
Dzeroski, S., “Knowledge discovery in a water quality database”,
KDD95
Ezawa, K., “Knowledge discovery in telecommunication services data
using Bayesian network models”, KDD95
Feelders, A., “Data mining for loan evaluation at ABN AMRO: a case
study”, KDD95
Sanjeev, A., “Discovering enrollment knowledge in university
databases”, KDD95
Tsumoto, S., “Automated discovery of functional components of
proteins from Amino-Acid sequences based on rough sets and change of
representation”, KDD95
Fitzsimons, M., “The application of rule induction and neural
networks for television audience prediction”, Proc. of ESOMAR/EMAC/AFM
symposium on information based decision making in marketing, 1993, pp.69-82
Schmitz, J., “CoverStory – automated news finding in marketing”, DSS
Transactions, ed. L. Volino, 46-54. Providence , R.I. : Institute
of Management
Anand, T., “Opportunity explorer:
navigating large databases using knowledge discovery templates”, JIIS 4(1):
27-38
Hall, J., “Applying computational intelligence to the investment
process”, Proc. of CIFER-96: computational intelligence in financial
engineering, IEEE Press
Senator, T., “The financial crimes enforcement network AI system
(FAIS)”, AI magazine, winter 1995, 21-39
Davis, A., “Management of cellular fraud: knowledge-based detection,
classification and prevention”, Proc. of 13th Int. Conf. on AI,
expert systems and natural language, v2, p.155-164
Data mining applications section in KDD96
網際網路資料探勘
Carbonell, J., “Learning from the WEB”, ISMIS 97
Chen, M.-S., “Data mining for path traversal patterns in a web
environment”, Int’l Conf. On Distributing Computing Systems, 1996 (COMPENDEX
91~)
Etzioni, O., “The World-Wide Web: quagmire or gold mine?”, CACM,
v.39, no.11, 1996
Hsu, Y.-J. and Wen-Tan Yih, (of Taiwan U.
Soderland, S. “Learning to extract text-based information from the
world wide web”, ICKDD 97
Zaiane, O., “Resource and knowledge discovery in global information
systems: a preliminary design and experiment”, KDD95
Zamir, O., “Fast and intuitive clustering of web documents”, ICKDD
97
IPO Keywords: world wide web AND information retrieval
文件資料探勘
Soderland, S. “Learning to extract text-based information from the
world wide web”, ICKDD 97
Hahn, U., “Deep knowledge mining from natural language text
sources”, (CIKM97)
Feldman, R., “Knowledge discovery in textual databases”, KDD95
Feldman, R., “Mining associations in text in the presence of
background knowledge”, KDD96
Feldman, R., “Document explorer: discovering knowledge in document
collections”, ISMIS 97
Zari, G., “Conceptual modeling of the “meaning” of textual narrative
documents”, ISMIS 97
Esposito, F., “Knowledge revision for document understanding”, ISMIS
97
Reuters-22173 corpus: a collection of 22,173 indexed documents
appearing on the Reuters newswire in 1987; Reuters Ltd, Carnegie Group, David
Lewis, Information Retrieval Laboratory at the University of Massachusetts;
available via ftp from: ciir-ftp.cs.umass.edu:/pub/reuters1/corpus.tar.Z.
簡立峰，中研院資科所中文資訊處理實驗室：Csmart系統
多媒體資料庫資料探勘
Ester, M., “A database interface for clustering in large spatial
databases”, KDD95
Li, C., “Knowledge-based scientific discovery in geological
databases”, KDD95
Stolorz, P., “Fast spatio-temporal data mining of large geophysical
datasets”, KDD95
Knorr, E., “Extraction of spatial proximity patterns by concept
generalization”, KDD96
Padmanabhan, B., “Pattern discovery in temporal databases: a
temporal logic approach”, KDD96
Czyzewski, A., “Mining knowledge in noisy audio data”, KDD96
Ester, M., “A density-based algorithm for discovering clusters in
large spatioal databases with noise”, KDD96
Kaufman, K., “A method for reasoning with stuctured and continuous
attributes in the INLEN-2 multistrategy knowledge discovery system”, KDD96
Lagus, K., “Self-organizing maps of document collections: a new
approach to interactive exploration”, KDD96
關連法則
Holsheimer, M., “A perspective on databases and data mining”, KDD95
Feldman, R., “Mining associations in text in the presence of background
knowledge”, KDD96
Cheung, D., “Maintenance of discovered knowledge: a case in
multi-level association rules”, KDD96
Agrawal, R., “Mining association rules between sets of items in
large databases”, ICMOD 1993
Agrawal, R., “Fast algorithms for mining association rules”, ICVLDB
94
Savasere, A., “An efficient algorithm for mining association rules
in large databases’, ICVLDB 95
Srikant, R., “Mining quantitative association rules in large
relational tables”, ICMOD 96
Fukuda, T., “Data mining using two-dimensional optimized association
rules: scheme, algorithms, and visualization”, ICMOD 96
Brin, S., “Dynamic itemset counting and implication rules for market
basket data”, ICMOD 97
Brin, S., “Beyond market baskets: generalizing association rules to
correlations”, ICMOD 97
Han, E.-H., “Scalable parallel data mining for association rules”,
ICMOD 97
Lent, B, “Clustering association rules”, ICDE 97
Park, J., “Mining association rules with adjustable accuracy”, CIKM
97
Singh, L., “Generating association rules from semi-structured
documents using a concept hierarchy”, CIKM 97
時間序列
Mannila, H., “Discovering frequent episodes in sequences”, KDD95
Mannila, H., “Discovering generalized episodes using minimal
occurrences”, KDD96
Mannila, H., “Rule discovery from time series”, KDD98
Agrawal, R. ``Efficient Similarity Search in Sequence Databases'',
4th Int'l Conf. on Foundations of Data Organization and Algorithms, 1993
Agrawal, R. ``Fast Similarity Search in the Presence of Noise,
Scaling, and Translation in Time-Series Databases'', 21st Int'l Conf. on VLDB,
1995.
Agrawal, R. ``Mining Sequential Patterns'', Int'l Conf. on Data
Engineering, 1995.
Agrawal, R., “Querying shapes of histories”, VLDB95 Proc.
Berndt, D., “Finding patterns in time series: a dynamic programming approach”,
Advances in KDD, 1996
Goldin, D., “On similarity queries for time-series data: constraint
specification and implementation”, 1st int’l conf. on the principles
and practice of constraint programming, LNCS 976, Sept. 1995
Jagadish, H., “Similarity-based queries”, PODS95 Proc.
Keogh, E., “A probabilistic approach to fast pattern matching in
time series databases”, KDD97
Laird, P., “Identifying and using patterns in sequential data”, 4th
Int’l Workshop on Algorithmic Learning Theory, 1993, Springer-Verlag, pp.1-18
Lent, B., “Discovering trends in text databases”, KDD97 Proc.
Rafiei, D., “Similarity-based queries for time series data”,
SIGMOD97 Proc.
Shim, K. "High-dimensional Similarity Joins", 13th Int'l
Conf. on Data Engineering, 1997.
Srikant, R. ``Mining Sequential Patterns: Generalizations and
Performance Improvements'', Fifth Int'l Conf. on Extending Database Technology,
1996.
Visualization and Data Exploration
Brunk, C., “MineSet: an integrated system for data mining”, ICKDD 97
Catarci, T., “Visual query systems for databases: a survey”, JVLC 97
Derthick, M., “An interactive visualization environment for data
exploration”, ICKDD 97
Feldman, R., “Visualization techniques to explore data mining
results for document collections”, ICKDD 97
Gebhardt, M., “A toolkit for negotiation support interfaces to
multi-dimensional data”, ICMOD97
Hee, H.-Y., “Visualization support for data mining”, IEEE EXPERT,
Oct. 1996
Livny, M., “DEVise: integrated querying and visual exploration of
large datasets”, ICMOD97
Mihalisin, T., “Fast robust visual data mining”, ICKDD97
Rao, S., “Providing better support for a class of decision support
queries”, ICMOD96
Roth, S., “Visage: a usr interface environment for exploring
information”, ICIV 96
Selfridge, P., “IDEA: interactive data exploration and analysis”,
ICMOD 96
Ahlberg, C., “Spotfire: an information exploration environment”,
SIGMODR v25 n4, Dec. 96
Kennedy, J., “A framework for information visualization”, SIGMODR
v25 n4, Dec. 96
Keim, D. “Pixel-oriented database visualizations”, SIGMODR v25 n4,
Dec. 96
Ioannidis, Y., “Dynamic information visualization”, SIGMODR v25 n4,
Dec. 96
Hasan, M., “Applying database visualization to the world wide web”,
SIGMODR v25 n4, Dec. 96
OLAP, Data Cube, and Data Warehousing
Chaudhuri, S., “An overview of data warehousing and OLAP
technology”, SIGMODR, March, 97
Colliat, G., “OLAP, relational, and multidimensional database
systems”, SIGMODR, Sept. 1996
Gray, J., “Data cube: a relational aggregation operator generalizing
group-by, cross-tab, and sub-totals”, JDMKD 97
Harinarayan, V., “Implementing data cubes efficiently”, ICMOD 96
Ho, C.-T., “Range queries in OLAP data cubes”, ICMOD 97
Roussopoulos, N., “Cubetree: organization of and bulk updates on the
data cube”, ICMOD97
Mumick, I. , “Maintenance of data
cubes and summary tables in a warehouse”, ICMOD97
Agrawal, R., “Modeling multidimensional databases”, ICDE 97
Gupta, H., “Index selection for OLAP”, ICDE 97
Labio, W., “Physical database design for data warehouses”, ICDE 97
Gyssens, M., “A foundation for multi-dimensional databases”, ICVLDB
97
Ross, K., “Fast computation of sparse datacubes”, ICVLDB 97
Clustering
Similarity
Weber, R. et al., A quantitative analysis
and performance study for similarity-search methods in high-dimensional spaces,
Int’l Conf. on VLDB, 1998.
Measures of interestingness
Kamber, M., “Evaluating the interestingness of characteristic
rules”, KDD96
Silberschatz, A., “On subjective measures of interestingness in
knowledge discovery”, KDD95
Suzuki, E., “Exceptional knowledge discovery in database based on
information theory”, KDD96
知識表示與資料探勘
人工智慧、專家系統教科書或知識表示法專書
Aronis, J., “Exploiting background knowledge in automated
discovery”, KDD96
特徵選擇
Kohavi, R., “Feature subset selection using the wrapper method:
overfitting and dynamic search space topology”, KDD95
Seshadri, V., “Feature extraction for massive data mining”, KDD95
Cherkauer, K., “Growing simpler decision trees to facilitate
knowledge discovery”, KDD96
Urpani, D., “RITIO - rule induction two in one”, KDD96