Data collection and processing progress made data mining a popular tool among organizations in the last decades. Sharing information between companies could make this more beneficial for each party. However, there is risk of sensitive knowledge disclosure. Shared should be modified such way that relationships would hidden. Since discovery frequent itemsets one most effective tools firms use, pr...

Penelitian ini di latar belakangi dari hasil pengamatan, saat dalam melakukan transaksi penjualan rata-rata sudah menggunakan sistem terkomputerisasi dan mencatat data yang ada, namun tersebut hanya berfungsi sebagai arsip saja sehingga belum bisa digunakan untuk memprediksi produk akan datang lebih diminati konsumen. Tujuan penelitian adalah menerapka mining konsumen mencari hubungan keterkait...

In this paper, we systematically explore an itemset-based extension approach for generating candidate sequence which contributes to a better and more straightforward search space traversal performance than traditional item-based extension approach. Based on this candidate generation approach, we present FINDER, a novel algorithm for discovering the set of all frequent sequences. FINDER is compo...

Closed frequent itemset mining plays an essential role in data stream mining. It could be used in business decisions, basket analysis, etc. Most methods for mining closed frequent itemsets store the streamlined information in compact data structure when data is generated. Whenever a query is submitted, it outputs all closed frequent itemsets. However, the online processing of existing approache...

The mining of frequent itemsets is often challenged by the length of the patterns mined and also by the number of transactions considered for the mining process. Another acute challenge that concerns the performance of any association rule mining algorithm is the presence of „null‟ transactions. This work proposes a closed frequent itemset mining algorithm viz., Closed Frequent Itemset Mining a...

Association rule mining is one of the vital data mining tasks to extract knowledge from the data. In the process of association rule mining the foremost step is to find the frequent itemset. The frequent itemset is used to generate association rules. In general brute –force approach is expensive because there are exponentially many rules that can be generated from the data set. So that support ...

The value of the itemset share is one way of evaluating the magnitude of an itemset. From business perspective, itemset share values reflect more the significance of itemsets for mining association rules in a database. The Share-counted FSM (ShFSM) algorithm is one of the best algorithms which can discover all share-frequent itemsets efficiently. However, ShFSM wastes the computation time on th...

Erasable itemset (EI) mining is an interesting variation of frequent itemset mining which allows managers to carefully consider their production plans to ensure the stability of the factory. Existing algorithms for EI mining require a lot of time and memory. This paper proposes an effective algorithm, called mining erasable itemsets (MEI), which uses the divide-and-conquer strategy and the diff...

1. Summary. In this paper the authors propose a differentially privacy preserving algorithm for mining frequent itemset. This work differs from the other privacy preserving miners present in literature, indeed this algorithm mines the itemset by enforcing cardinality constraints on the transactions present in the dataset. In particular the authors study how the reduction the cardinality of the ...