Recently, a quantum algorithm for fundamentally important task in data mining, association rules mining (ARM), called qARM short, has been proposed. Notably, achieves significant speedup over its classical counterpart implementing the main of ARM, i.e., finding frequent itemsets from transaction database. In this paper, we experimentally implement on both real computers and computing simulator ...

This study applied a prediction-based portfolio optimization model to explore the results of portfolio predicament in the Tehran Stock Exchange. To this aim, first, the data mining approach was used to predict the petroleum products and chemical industry using clustering stock market data. Then, some effective factors, such as crude oil price, exchange rate, global interest rate, gold price, an...

Mining association rules in the database is one of important study in data mining research. Traditional association rules consist of some redundant information, and need scan database many times and generate lots of candidate item sets. Aiming at low efficiency in association rules mining using traditional methods, this paper proposes the algorithm (ISMFP), which is based on intersection for mi...

The traditional data mining algorithm focuses too much on a single dimension of time or space, ignoring the association between and which leads to large amount computation low processing efficiency makes it difficult guarantee final effect. In response above problems, hierarchical based rules for high-dimensional spatio-temporal big is proposed. Based rules, after establishing data, be mined ar...

the rapid growing of information technology (it) motivates and makes competitive advantages in health care industry. nowadays, many hospitals try to build a successful customer relationship management (crm) to recognize target and potential patients, increase patient loyalty and satisfaction and finally maximize their profitability. many hospitals have large data warehouses containing customer ...

Classification is the supervised learning algorithm that maps the data into predefined groups & classes. Clustering is unsupervised algorithm which finds groups of objects such that the objects in one group will be similar to one another and different from the objects in another group while Association Rule algorithms are used to show the relationship between the data items. In this paper we pr...

Frequent itemset mining and association rule generation is a challenging task in data stream. Even though, various algorithms have been proposed to solve the issue, it has been found out that only frequency does not decides the significance interestingness of the mined itemset and hence the association rules. This accelerates the algorithms to mine the association rules based on utility i.e. pr...

The tremendous development of location-based services and mobile devices has led to an increase in location databases. Through the data mining process, valuable information can be discovered from such location databases. However, the malicious data miner or attackers may also extract private and sensitive information about the user, and this can create threats against the user location privacy....