Recommended Readings
·
R. Agrawal,
T. Imielinski, and A. Swami. Mining
association rules between sets of items in large databases. SIGMOD,
207-216, 1993.
·
R. Agrawal
and R. Srikant. Fast
algorithms for mining association rules. VLDB, 487-499, 1994.
·
S. Brin,
R. Motwani, J. D. Ullman,
and S. Tsur. Dynamic itemset counting and implication rules for market basket analysis.
SIGMOD, 255-264, 1997.
·
J.S. Park, M.S. Chen, and P.S. Yu. An effective
hash-based algorithm for mining association rules. SIGMOD, 175-186, 1995.
·
A. Savasere,
E. Omiecinski, and S. Navathe.
An
efficient algorithm for mining association rules in large databases. VLDB,
432-444, 1995.
·
H. Toivonen.
Sampling
large databases for association rules. VLDB, 134-145, 1996.
·
R. J. Bayardo.
Efficiently
mining long patterns from databases. SIGMOD, 85-93, 1998.
·
N. Pasquier,
Y. Bastide, R. Taouil, and
L. Lakhal. Discovering
frequent closed itemsets for association rules.
ICDT, 398-416, 1999.
·
J. Pei, J. Han, and R. Mao. CLOSET: An
Efficient Algorithm for Mining Frequent Closed Itemsets.
DMKD, 11-20, 2000.
·
R. Agarwal,
C. Aggarwal, and V. V. V. Prasad. A tree projection algorithm for
generation of frequent itemsets. Parallel and
Distributed Computing, 61(3):350-371, 2001.
·
J. Han, J. Pei, and Y. Yin. Mining frequent
patterns without candidate generation. SIGMOD, 1-12, 2000.
·
R. Srikant
and R. Agrawal. Mining sequential
patterns: Generalizations and performance improvements. EDBT, 3-17, 1996.
·
J. Pei, J. Han, H. Pinto, Q. Chen,
U. Dayal, and M.-C. Hsu. PrefixSpan: Mining sequential patterns efficiently by
prefix-projected pattern growth. ICDE, 215-224, 2001.
·
Q. Zhao, S. Bhowmick.
Sequential Pattern
Mining: A Survey, 2006
·
P.
Berkhin. Survey
of clustering data mining techniques, 2002.
·
R.
Ng and J. Han. Efficient
and effective clustering method for spatial data mining. VLDB, 144-155,
1994.
·
T. Zhang, R. Ramakrishnan,
and M. Livny. BIRCH : an efficient data clustering method for very large
databases. SIGMOD, 103-114, 1996.
·
S. Guha, R. Rastogi, and K. Shim. Cure: an efficient clustering
algorithm for large databases. SIGMOD, 73-84, 1998.
·
M. Ester, H.-P. Kriegel,
J. Sander, and X. Xu. A
density-based algorithm for discovering clusters in large spatial databases.
KDD, 226-231, 1996.
·
W. Wang, J. Yang, and R. Muntz.
STING: a statistical
information grid approach to spatial data mining. VLDB, 186-195, 1997.
·
G. Sheikholeslami,
S. Chatterjee, and A. Zhang. WaveCluster:
a multi-resolution clustering approach for very large spatial databases.
VLDB, 428-439, 1998.
·
R.
Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic
subspace clustering of high dimensional data for data mining. SIGMOD,
94-105, 1998.
·
S.
K. Murthy. Automatic
construction of decision trees from data: A multi-disciplinary survey, data
mining and knowledge discovery. KDD Journal, 2(4), 345-389, 1998.
‘
·
http://en.wikipedia.org/wiki/Bayesian_network
·
P.
Spirtes and C. Glymour, An algorithm
for fast recovery of sparse causal graphs, Social Science Computer Review, Vol. 9, pp. 62-72, 1991
·
Pearl,
Judea, Causality: Models,
Reasoning, and Inference. Cambridge University Press. ISBN
0-521-77362-8. 2000
Classification Part
III
·
C.
J. C. Burges. A
Tutorial on Support Vector Machines for Pattern Recognition. Data Mining
and Knowledge Discovery, 2(2), 121-168, 1998.
·
B.
Liu, W. Hsu, and Y. Ma. Integrating
classification and association rule mining. KDD, 1998.