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Sequential patterns mining is an important research topic in data mining and knowledge discovery. The objective of mining sequential patterns is to find out frequent sequences based on the user-specified minimum support threshold, which implicitly assumes that all items in the data have similar frequencies. This is often not the case in real-life applications. If the frequencies of items vary a great...
While there has been a lot of work on finding frequent item sets in transaction data streams, none of these solve the problem of finding similar pairs according to standard similarity measures. This paper is a first attempt at dealing with this, arguably more important, problem. We start out with a negative result that also explains the lack of theoretical upper bounds on the space usage of data mining...
An approach is proposed to discover closed frequent itemsets with a simple linear list structure called the Frequent Pattern List(FPL) in transaction database. The approach selects representation patterns from candidate itemsets to reduce combinational space of frequent patterns. By performing two operations, signature vertex conjunction and vertex counting, it simplify the process of closed itemsets...
There are two shortages in usual methods for agricultural land evaluation: (1) too many manual interferences into the calculating procession, (2) the relatively large differences of partial units are concealed in certain factors. We designed a hyper graph clustering model in this paper based on fuzzy frequent item sets to conduct the evaluation for quality of agricultural land. The database for land...
With the rapid development of online shopping, on-line one-to-one marketing becomes a great assistance to e-shoppers. One of the most important marketing resources is the prior daily transaction records in the database. In this study, the paper propose a new methodology for predict e-shoppers' purchase behavior that uses e-shoppers' purchase sequences. First, transaction clustering is conducted, then...
Detailed inspection of transactional data can reveal various useful information, in which of special importance are relationships between transaction elements. Hierarchical clustering coupled with specific distance measures reveal those relationships from one angle. Additionally, association rules - a natural method of inspecting transactional data - is able to reveal relationships between each pair...
Most of the incremental association rule mining methods must rerun through processed data and have not made the best of the given rules. In this paper we propose an incremental association rules algorithm, this algorithm applies artificial immune theory and takes advantage of the given rules produced by original data set. Based on the quickly response during the memory cell recognizing the antigen,...
This paper considers the problem of privacy preserving transaction data publishing. Transaction data are usually useful for data mining. While it is high-dimensional data, traditional anonymization techniques such as generalization and suppression are not suitable. In this paper, we present a novel technique based on anatomy technique and propose a simple linear-time anonymous algorithm that meets...
Application of data mining techniques in library data results interesting and useful patterns that can be used to improve services in university libraries. This paper presents results of the work in applying the sequential pattern mining algorithm namely AprioriAll on a library transaction dataset. Frequent sequential patterns containing book sequences borrowed by students are generated for minimum...
Among a large number of association rule mining algorithms, Apriori algorithm is the most classic one, but the Apriori algorithm has three deficiencies, namely: the need for scanning databases many times, generating a large number of Candidate Anthology, as well as frequent itemsets iteratively. The paper presents a method that solves the maximal frequent itemsets through one intersection operation...
In this paper, we design a new kind of patterns, named high transaction-weighted utility itemsets, which considers not only individual profits and quantities of the items in a transaction, but also the contribution of each transaction in a database. We also propose a two-phased mining algorithm to discover high transaction-weighted utility itemsets. The experimental results on synthetic datasets show...
Data mining (DM) is the process of automated extraction of interesting data patterns representing knowledge, from the large data sets. Frequent itemsets are the itemsets that appear in a data set frequently. Finding such frequent itemsets plays an essential role in mining associations, correlations, and many other interesting relationships among itemsets in transactional database. In this paper an...
Data mining techniques are used for the knowledge discovery process under the large data set environment. Clustering techniques are used to group up the relevant data sets. Hierarchical and partitioned clustering techniques are used for the clustering process. The clustering process is the complex task with high process time. The pattern extraction scheme is applied to find frequent item sets. Association...
This paper firstly analyzes the current measures of association rules and then proposes a measure of match as the substitution of confidence. Considering that there are often two kinds of situations in transaction database: (1) the importance of different itemsets are different. (2) When new data are added, how to reflect the popular information of the added database while guaranteeing the correlation...
Notice of Violation of IEEE Publication Principles"An Efficient Mining Algorithm for Top K Strongly Correlated Item Pairs"by Qiang Li and Yongshi Zhangin the Proceedings of the 4th International Conference on Internet Computing for Science and Engineering, December 2009, pp. 152-155After careful and considered review of the content and authorship of this paper by a duly constituted expert...
Many organizations collect information about customers which are used to support various business related task. The detail of the customers and their behavior is stored in the database. Here we propose a method of using incremental updating technique to mine direct association rules Inter & Intra transactions. Cluster analysis is performed to verify the associated objects fall in nominal cluster...
In order to improve the privacy preservation and the mining efficiency, an effective privacy preserving distributed mining algorithm of association rules is proposed in this paper. Combining the advantages of both RSA public key cryptosystem and homomorphic encryption scheme, a model of hierarchical management on the cryptogram is put forward in the algorithm. By introducing cryptogram management...
Firstly, in this paper we propose an improved immune algorithm, that is, introduce the Metropolis criterion into the selection operation of immune algorithm, and the Metropolis immune algorithm (MIA) is formed, then we carry out the theoretical analysis and experimental simulation aiming at the performance of the MIA; secondly, we use this algorithm to excavate association rules, and propose a new...
It is very necessary for e-marketers to understand e-shoppers' needs for making correct marketing strategies. Knowledge of e-shoppers purchase behaviors can be extracted from transaction databases. In this paper, transaction database is transformed into the database of linguistic values, and research on e-shopper purchase behavior is made based on linguistic values of product attributes. And an algorithm...
To increase the efficiency of data mining is the research emphasis in this field at present. Through the establishment of transaction-item association matrix, this paper changes the process of association rule mining to elementary matrix operation, which makes the process of data mining clear and simple. Compared with algorithms like Apriori, this method avoids the demerit of traversing the database...
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