Results 1  10
of
49
An effective hashbased algorithm for mining association rules
, 1995
"... In this paper, we examine the issue of mining association rules among items in a large database of sales transactions. The mining of association rules can be mapped into the problem of discovering large itemsets where a large itemset is a group of items which appear in a sufficient number of transac ..."
Abstract

Cited by 283 (3 self)
 Add to MetaCart
of transactions. The problem of discovering large itemsets can be solved by constructing a candidate set of itemsets first and then, identifying, within this candidate set, those itemsets that meet the large itemset requirement. Generally this is done iteratively for each large kitemset in increasing order of k
Using a HashBased Method with Transaction Trimming and Database Scan Reduction for Mining Association Rules
 IEEE Transactions on Knowledge and Data Engineering
, 1997
"... In this paper, we examine the issue of mining association rules among items in a large database of sales transactions. Mining association rules means that given a database of sales transactions, to discover all associations among items such that the presence of some items in a transaction will imply ..."
Abstract

Cited by 83 (9 self)
 Add to MetaCart
by constructing a candidate set of itemsets first and then, identifying, within this candidate set, those itemsets that meet the large itemset requirement. Generally this is done iteratively for each large kitemset in increasing order of k where a large kitemset is a large itemset with k items. To determine
SETM*MaxK: An Efficient SETBased Approach to Find the Largest Itemset
"... Abstract. In this paper, we propose the SETM*MaxK algorithm to find the largest itemset based on a highlevel setbased approach, where a large itemset is a set of items appearing in a sufficient number of transactions. The advantage of the setbased approach, like the SETM algorithm, is simple and ..."
Abstract
 Add to MetaCart
and stable over the range of parameter values. In the SETM*MaxK algorithm, we efficiently find the Lk based on Lw, where Lk denotes the set of large kitemsets with minimum support, Lk = ∅, Lk+1 = ∅ and w = 2log2k−1, instead of step by step. From our simulation, we show that the proposed SETM*MaxK
Mining The Topk Frequent Itemset With Minimum Length M
, 2001
"... With the explosive growth of data stored in electronic form, data mining has become essential in searching nontrivial, implicit, previously unknown and potentially useful information from a huge amount of data. Association rule mining in large transactional databases is an important topic in the ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
itemsets which satisfy a minimum support threshold, minsup. Association rules can then be generated easily. In this work, I propose two interesting frequent itemset mining algorithms, DIPT and FIPT, which find the topk frequent itemsets with the minimum length m in the transaction database
An Eective HashBased Algorithm for Mining Association Rules
"... fcpark mschen psyug watsonibmcom In this paper we examine the issue of mining association rules among items in a large database of sales transactions The mining of association rules can be mapped into the problem of discovering large itemsets where a large itemset is a group of items which appear ..."
Abstract

Cited by 7 (0 self)
 Add to MetaCart
kitemset in increasing order of k where a large kitemset is a large itemset with k items To determine large itemsets from a huge number of candidate large itemsets in early iterations is
A fast Parallel Association Rule Mining Algorithm Based on the Probability of Frequent Itemsets
"... Summary Frequent itemset finding is the most costly processing step in analyzing large transactional databases. At each stage in discovering frequent itemset a huge number of candidate itemsets are produced. Then, if we predict which candidate itemset will be frequent and which will not, we can red ..."
Abstract
 Add to MetaCart
and makes priority between candidate itemsets base on it's probability. Moreover, the PFI algorithm passes the database only one time by dividing the database horizontally and distributes it over the system nodes. Also, while finding the kitemsets, the algorithm can start a new stage (finding k+1
Background for Association Rules and Cost Estimate of Selected Abstract Mining Algorithms
"... Association rules may be used to represent regular patterns in databases for the purpose of decision suppori applications. Fast algorithms for mining association rules have been proposed and studied experimentally in the literature. A key to the algorithms is to find large itemsets, i.e., sets of it ..."
Abstract
 Add to MetaCart
of Aprioti, AprioriTid, AprioriHybrid, OCD, SETM and DHP algorithms and study their scalability. Our study suggeststhat the key to costs and scalability is the space complexity of large itemsets and candidate itemsets. If the size of candidate kitemsets is less than main memory, then the above algorithms
Discovering Frequent Substructures In Large Unordered Trees
 IN PROC. OF THE 6TH INTL. CONF. ON DISCOVERY SCIENCE
, 2003
"... In this paper, we study a data mining problem of discovering frequent substructures in a large collection of semistructured data, where both of the patterns and the data are modeled by labeled unordered trees. An unordered tree is a directed acyclic graph with a specified node called the root, ..."
Abstract

Cited by 51 (6 self)
 Add to MetaCart
In this paper, we study a data mining problem of discovering frequent substructures in a large collection of semistructured data, where both of the patterns and the data are modeled by labeled unordered trees. An unordered tree is a directed acyclic graph with a specified node called the root
Fully automatic crossassociations
 In KDD
, 2004
"... Large, sparse binary matrices arise in numerous data mining applications, such as the analysis of market baskets, web graphs, social networks, cocitations, as well as information retrieval, collaborative filtering, sparse matrix reordering, etc. Virtually all the popular methods for analysis of suc ..."
Abstract

Cited by 98 (31 self)
 Add to MetaCart
of such matrices—e.g., kmeans clustering, METIS graph partitioning, SVD/PCA and frequent itemset mining—require the user to specify various parameters, such as the number of clusters, number of principal components, number of partitions, and “support. ” Choosing suitable values for such parameters is a
Parallel Mining of Maximal Frequent Itemsets from Databases
 In Tools with Artificial Intelligence, 2003. Proceedings. 15th IEEE International Conference on, pages 134 – 139
, 2003
"... In this paper, we propose a parallel algorithm for mining maximal frequent itemsets from databases. A frequent itemset is maximal if none of its supersets is frequent. The new parallel algorithm is named Parallel MaxMiner (PMM), and it is a parallel version of the sequential MaxMiner algorithm [3 ..."
Abstract

Cited by 7 (0 self)
 Add to MetaCart
[3]. Most of existing mining algorithms discover the frequent kitemsets on the kth pass over the databases, and then generate the candidate (k + 1)itemsets for the next pass. Compared to those levelwise algorithms, PMM looks ahead at each pass and prunes more candidate itemsets by checking
Results 1  10
of
49