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AN EFFICIENT SET-BASED APPROACH TO MINING ASSOCIATION RULESHsieh, Yu-Ming 28 July 2000 (has links)
Discovery of {it association rules} is an important problem in the area of data
mining. Given a database of sales transactions, it is desirable to discover
the important associations among items such that the presence of some items
in a transaction will imply the presence of other items in the same
transaction.
Since mining association rules may require to repeatedly scan through a large
transaction database to find different association patterns, the amount of
processing could be huge, and performance improvement is an essential
concern.
Among this problem, how to efficiently {it count large
itemsets} is the major work, where a large itemset is a set of items
appearing in a sufficient number of transactions.
In this thesis, we propose efficient algorithms for mining association
rules based on a high-level set-based approach.
A set-based approach allows a clear expression of what needs to be done
as opposed to specifying exactly how the operations are carried out in a low-level approach, where
a low-level approach means to retrieve one tuple from the database at a time.
The advantage of the set-based approach, like the SETM algorithm,
is simple and stable over the range of parameter values.
However, the SETM algorithm proposed by Houtsma and Swami may generate too many invalid candidate itemsets.
Therefore, in this thesis, we propose a set-based algorithm called SETM*,
which provides the same advantages of the SETM algorithm,
while it avoids the disadvantages of the SETM algorithm.
In the SETM* algorithm, we reduce the size of the candidate database by
modifying the way of constructing it,
where a candidate database is a transaction database formed with candidate
$k$-itemsets.
Then, based on the new way to construct the candidate database in the SETM*
algorithm, we propose SETM*-2K, mbox{SETM*-MaxK} and SETM*-Lmax algorithms.
In the SETM*-2K algorithm, given a $k$, we efficiently construct $L_{k}$
based on $L_{w}$, where $w=2^{lceil log_{2}k
ceil - 1}$,
instead of step by step.
In the SETM*-MaxK algorithm, we efficiently to find the $L_{k}$ based on $L_{w}$,
where $L_{k}
ot= emptyset, L_{k+1}=emptyset$ and $w=2^{lceil log_{2}k
ceil - 1}$,
instead of step by step.
In the SETM*-Lmax algorithm, we use a forward approach to find all maximal large itemsets from $L_{k}$,
and the $k$-itemset is not included in the $k$-subsets of the $j$-itemset,
except $k=MaxK$, where $1 leq k < j leq MaxK$,
$L_{MaxK}
ot= emptyset$ and $L_{MaxK+1}=emptyset$.
We conduct several experiments using different synthetic relational databases.
The simulation results show that the
SETM* algorithm outperforms the SETM algorithm in terms of storage space or the
execution time for all relational database settings.
Moreover, we show that the proposed SETM*-2K
and SETM*-MaxK algorithms also require shorter time to achieve their goals than the SETM or SETM* algorithms.
Furthermore, we also show that the proposed forward approach (SETM*-Lmax)
to find all maximal large itemsets requires shorter time than the backward approach proposed by Agrawal.
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Novel applications of association rule mining- data stream mining this thesis is submitted as part of degree of Master of Computer and Information Sciences at the Auckland University of Technology, August 2009 /Vithal Kadam, Omkar. January 2009 (has links)
Thesis (MCIS)--AUT University, 2009. / Includes bibliographical references. Also held in print (72 leaves : ill. ; 30 cm.) in the Archive at the City Campus (T 006.312 VIT)
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Mining association rules with weighted itemsCai, Chun Hing. January 1998 (has links) (PDF)
Thesis (M. Phil.)--Chinese University of Hong Kong, 1998. / Description based on contents viewed Mar. 13, 2007; title from title screen. Includes bibliographical references (p. 99-103). Also available in print.
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New data mining models based on formal concept analysis and probability logicJiang, Liying. January 1900 (has links)
Thesis (Ph.D.)--University of Nebraska-Lincoln, 2006. / Title from title screen (site viewed on Jan 23, 2007). PDF text: 127 p. : ill. (some col.) ; 1.29Mb. UMI publication number: AAT 3216105. Includes bibliographical references. Also available in microfilm and microfiche format.
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Apriori sets and sequences mining association rules from time sequence attributes.Pray, Keith A. January 2004 (has links)
Thesis (M.S.) -- Worcester Polytechnic Institute. / Keywords: mining complex data; temporal association rules; computer system performance; stock market analysis; sleep disorder data. Includes bibliographical references (p. 79-85).
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noneChen, Chun-Yu 20 January 2009 (has links)
none
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Association rule mining in cooperative researchZhang, Ya. Klein, Cerry M. January 2009 (has links)
The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed January 26, 2010). Thesis advisor: Dr. Cerry M. Klein. Includes bibliographical references.
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Two new approaches to evaluate association rulesDelpisheh, Elnaz, University of Lethbridge. Faculty of Arts and Science January 2010 (has links)
Data mining aims to discover interesting and unknown patterns in large-volume data. Association rule mining is one of the major data mining tasks, which attempts to find inherent relationships among data items in an application domain, such as supermarket basket analysis. An essential post-process in an association rule mining task is the evaluation of association rules by measures for their interestingness. Different interestingness measures have been proposed and studied. Given an association rule mining task, measures are assessed against a set of user-specified properties. However, in practice, given the subjectivity and inconsistencies in property specifications, it is a non-trivial task to make appropriate measure selections. In this work, we propose two novel approaches to assess interestingness measures. Our first approach utilizes the analytic hierarchy process to capture quantitatively domain-dependent requirements on properties, which are later used in assessing measures. This approach not only eliminates any inconsistencies in an end user’s property specifications through consistency checking but also is invariant to the number of association rules. Our
second approach dynamically evaluates association rules according to a composite and
collective effect of multiple measures. It interactively snapshots the end user’s domain-
dependent requirements in evaluating association rules. In essence, our approach uses
neural networks along with back-propagation learning to capture the relative importance
of measures in evaluating association rules. Case studies and simulations have been conducted to show the effectiveness of our two approaches. / viii, 85 leaves : ill. ; 29 cm
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Novel applications of Association Rule Mining- Data Stream MiningVithal Kadam, Omkar January 2009 (has links)
From the advent of association rule mining, it has become one of the most researched areas of data exploration schemes. In recent years, implementing association rule mining methods in extracting rules from a continuous flow of voluminous data, known as Data Stream has generated immense interest due to its emerging applications such as network-traffic analysis, sensor-network data analysis. For such typical kinds of application domains, the facility to process such enormous amount of stream data in a single pass is critical.
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Novel applications of Association Rule Mining- Data Stream MiningVithal Kadam, Omkar January 2009 (has links)
From the advent of association rule mining, it has become one of the most researched areas of data exploration schemes. In recent years, implementing association rule mining methods in extracting rules from a continuous flow of voluminous data, known as Data Stream has generated immense interest due to its emerging applications such as network-traffic analysis, sensor-network data analysis. For such typical kinds of application domains, the facility to process such enormous amount of stream data in a single pass is critical.
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