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6.6 Bibliographic Notes 277

[MTV94]. A joint publication combining these works later appeared in Agrawal,
+
Mannila, Srikant et al. [AMS 96]. A method for generating association rules from
frequent itemsets is described in Agrawal and Srikant [AS94a].
References for the variations of Apriori described in Section 6.2.3 include the
following. The use of hash tables to improve association mining efficiency was stud-
ied by Park, Chen, and Yu [PCY95a]. The partitioning technique was proposed by
Savasere, Omiecinski, and Navathe [SON95]. The sampling approach is discussed in
Toivonen [Toi96]. A dynamic itemset counting approach is given in Brin, Motwani,
Ullman, and Tsur [BMUT97]. An efficient incremental updating of mined association
rules was proposed by Cheung, Han, Ng, and Wong [CHNW96]. Parallel and dis-
tributed association data mining under the Apriori framework was studied by Park,
Chen, and Yu [PCY95b]; Agrawal and Shafer [AS96]; and Cheung, Han, Ng, et al.
+
[CHN 96]. Another parallel association mining method, which explores itemset clus-
tering using a vertical database layout, was proposed in Zaki, Parthasarathy, Ogihara,
and Li [ZPOL97].
Other scalable frequent itemset mining methods have been proposed as alterna-
tives to the Apriori-based approach. FP-growth, a pattern-growth approach for mining
frequent itemsets without candidate generation, was proposed by Han, Pei, and Yin
[HPY00] (Section 6.2.4). An exploration of hyper structure mining of frequent patterns,
+
called H-Mine, was proposed by Pei, Han, Lu, et al. [PHL 01]. A method that integrates
top-down and bottom-up traversal of FP-trees in pattern-growth mining was proposed
by Liu, Pan, Wang, and Han [LPWH02]. An array-based implementation of prefix-
tree structure for efficient pattern growth mining was proposed by Grahne and Zhu
[GZ03b]. Eclat, an approach for mining frequent itemsets by exploring the vertical data
format, was proposed by Zaki [Zak00]. A depth-first generation of frequent itemsets by
a tree projection technique was proposed by Agarwal, Aggarwal, and Prasad [AAP01].
An integration of association mining with relational database systems was studied by
Sarawagi, Thomas, and Agrawal [STA98].
The mining of frequent closed itemsets was proposed in Pasquier, Bastide, Taouil,
and Lakhal [PBTL99], where an Apriori-based algorithm called A-Close for such min-
ing was presented. CLOSET, an efficient closed itemset mining algorithm based on
the frequent pattern growth method, was proposed by Pei, Han, and Mao [PHM00].
CHARM by Zaki and Hsiao [ZH02] developed a compact vertical TID list structure
called diffset, which records only the difference in the TID list of a candidate pattern
from its prefix pattern. A fast hash-based approach is also used in CHARM to prune
nonclosed patterns. CLOSET+ by Wang, Han, and Pei [WHP03] integrates previously
proposed effective strategies as well as newly developed techniques such as hybrid tree-
projection and item skipping. AFOPT, a method that explores a right push operation on
FP-trees during the mining process, was proposed by Liu, Lu, Lou, and Yu [LLLY03].
Grahne and Zhu [GZ03b] proposed a prefix-tree–based algorithm integrated with
array representation, called FPClose, for mining closed itemsets using a pattern-growth
approach.
+
Pan, Cong, Tung, et al. [PCT 03] proposed CARPENTER, a method for finding
closed patterns in long biological data sets, which integrates the advantages of vertical