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Web data mining is a key tool for e-commerce in such an age of Internet. Due to previous studies, there is no such a mining technology superior to others. Therefore, this paper can give a simplified comprehension of web data mining and indicate the improvement direction of each kind of mining algorithm based on their existing defects. This paper first introduces the main process of data mining, including...
The Moroccan financial system has undergone major changes since the early 90s. The financial market authority makes available a multitude of public information and statistics on the financial operations of the issuers. Other than the classification by sector or by type and / or amount of the issue, there is no classification model to predict the behavior of an issuer based on financial indicators...
In this paper a system for voice of customer analysis is proposed, which will produce strong rules to help organization to take business decisions. It uses parallel association rule mining for rule generation and data usually tends to be very huge so partitioning is done on the basis of sentiment of customer. For this purpose text mining algorithm is used which extracts information from unstructured...
In association rule mining, support is a measure of association between two sets of items, which indicates the relative occurrence of both sets within the overall set of transactions. In this paper, we propose a support-based vertical partitioning method that is easy to implement and can find an optimal vertical partitioning scheme. We present several experimental results to clarify the validness...
Most association rule mining research focuses on finding positive relationships between items. However, many studies in intelligent data analysis indicate that negative association rules are as important as positive ones. Therefore, we propose a method improved upon the traditional negative association rule mining. Our method mainly decreases the huge computing cost of mining negative association...
A novel approach to construct fuzzy classification system based on fuzzy association rules is proposed in this paper. Competitive agglomeration algorithm is employed to partition quantitative attributes from each data record into several optimized fuzzy sets, resulting in an initial fuzzy classification system. A fuzzy classification system with high accuracy and interpretability can be further achieved...
In this paper we attempt to maximize the efficiency of the parallel Apriori Algorithm. The paper analyzes the performance of the algorithm over different datasets and over n processors on a commodity cluster of machines. In the Apriori Algorithm all processes need to synchronize after every pass. If any process is assigned more load than other processes in the system, the slowest process will dictate...
On the basis of in-depth study of the existing data mining algorithms, according to the disadvantages of them in relational databases, in this paper a new data mining algorithm based on association rules is presented. The algorithm can avoid redundant rules as far as possible, and the experimental results show that the performance of the algorithm can be obviously improved when compared with the existing...
This paper introduces the application of association rules methods in data mining and the FP-tree algorithm for mining association rules in commercial sales analysis. It begins with the discussion of the concepts of association rules, followed by the analysis of mining for association rules and the FP-tree algorithm, and ended with their applications in commercial sales analysis as well as the demonstration...
This paper mainly focuses on the need, merits, demerits and designing of different sequential, parallel and distributed ARM algorithms developed so far on different hardware platforms and categorising them according to database format used, search techniques used and whether they find all or maximal frequent item sets. The goal of this survey is to provide information that serve as a reference for...
This paper presents TP-PB algorithm. It applies two phases association rule mining one based partition and Bitset for massive remote sensing images data. Firstly, this algorithm divides massive database into several independent blocks logically. Boolean values are stored in the compressed BitSet in each of block. It generates frequent itemsets by Bitset logical AND operation replaces database scans...
Mining the frequent item sets is the core of algorithm for mining association rules. Therefore, to improve the efficiency of discovering the frequent item sets is a key issue in data mining area. In this paper, an algorithm for acquiring the frequent item sets, which is based on the set operations of item-transaction list, is put forward. It turns out to cost less in terms of both space and time....
Many algorithms have been proposed to solve the problem of mining frequent itemset. The resulting frequent itemsets represent the global frequent patterns. This global output doesn't provide any information about the distribution of the frequent patterns on the database. This missing information can produce inaccurate decisions or prediction when the output frequent itemsets are used in decision support...
Association rule mining is one of the most important and basic technique in data mining, which has been studied extensively and has a wide range of applications. Two stream of previous work has dealt with the discovery of association rules over multiple relations: prolog databases and datalog queries. The MRI Iceberg-cubes mining method introduces a new perspective. However, it does not take the cyclic...
Based on DHP (direct hashing and pruning) algorithm, this paper presents a kind of transaction-marked DHP algorithm (TMDHP for short) to mining frequent itemsets in pervasive computing. Each element of the itemsets and the transaction's ID will be stored together in the hash-table. Using this method just need to access database once and avoids producing a deal of candidate itemsets. The experiments...
Mining association rules plays an essential role in data mining tasks. Many algorithms have been proposed for mining Boolean association rules, but they cannot deal with quantitative and categorical data directly. Although we can transform quantitative attributes into intervals and applying Boolean algorithms to the intervals. But this approach is not effective and is difficult to scale up for high-dimensional...
Discovering frequent patterns is one of the essential topic data mining. A new algorithm based on the two-way-hybrid search for frequent itemsets mining is proposed. 1) A hierarchical search space organization is presented, based on which the original search space can be recursively decomposed into some smaller independent pieces. 2) A novel HFMI algorithm, which explores a flexible two-way-hybrid...
In true-life the database is changed continually in many applications. Incremental mining technique has been developed to avoid rescanning database for knowledge discovery. Recent and compact constraints also are developed for frequent patterns mining. We store the database with a time-vertical bitmap representation, therefore the supports of frequent pattern and recent pattern can be computed fast...
Parallel system is mainly composed of parallel algorithms which are cost optimal. In this paper a parallel algorithm the hash partitioned apriori (HPA) is taken into consideration. HPA partitions the candidate itemsets among processors using a hash function, like the hash join in relational databases. HPA effectively utilizes the whole memory space of all the processors, hence it works well for large...
A new parallel algorithm for finding the frequent itemsets in databases is presented. It differs fundamentally of well known Apriori algorithm, where at the beginning of every step, the dimension of the new frequent itemsets increases by 1 . In our algorithm the frequent itemsets are determined by progressively enlarging the interval which the individual items appertain, i.e. if at the k-th step the...
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