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One of the interesting and important subjects among researchers in the field of medical and computer science is diagnosing illness by considering the features that have the most impact on recognitions. The subject discusses a new concept which is called Medical Data Mining (MDM). Indeed, data mining methods use different ways such as classification and clustering to classify diseases and their symptoms...
Web recommendation systems are helpful in overcoming the excess information on web by retrieving the information required by the user with respect to user's or similar users' preferences and interests. In order to make web recommendation system work, web users have to be clustered based on their common interest. The web user clusters are used to obtain the knowledge about the web pages accessed. This...
To come over the limitations of Apriori algorithm and association rule mining algorithm based on Genetic Algorithm (GA), this paper proposed a new association rule mining algorithm based on the population-based incremental algorithm (PBIL), which is a kind of distribution estimation algorithms. The proposed association rule-mining algorithm keeps the advantages of GA mining association rules in coding...
Semi-supervised learning is the required paradigm when data are partially labeled. It is more adapted for large domain applications when labels are hardly and costly to obtain. In addition, when data are large, feature selection and instance selection are two important dual operations for removing irrelevant information. To address theses challenges together, we propose a unified framework, called...
The feature subset selection, along with the parameters of classifier significantly influences the classification accuracy. In order to ensure the optimal classification performance, the artificial bee colony (ABC) algorithm is proposed to simultaneously optimize the feature subset and the parameters of support vector machines (SVM), meanwhile for improving the optimizing performance of ABC algorithm,...
Today, development of internet causes a fast growth of internet shops and retailers and makes them as a main marketing channel. This kind of marketing generates a numerous transaction and data which are potentially valuable. Using data mining is an alternative to discover frequent patterns and association rules from datasets. In this paper, we use data mining techniques for discovering frequent customers'...
Data mining is one of the most important steps in knowledge discovery. Apriori algorithm is the most used one in this process. The major drawback with Apriori algorithm is of time and space. It generates numerous uninteresting itemsets which lead to generate various rules which are of completely of no use. The two factors considered for association rules generation are Minimum Support Threshold and...
In order to solve the problem that the customer's personalized need are not fully considered in the existing multi-level logistics distribution network, the combination of customer's potential demand with the customer's personalized need that introduced into the logistics network is proposed. According to the customer's network score data to predict the potential need of customer, and combined with...
Specific crime in the banking system is credit card fraud. Credit card usage has been increased due to the rapid growth of E-commerce techniques. Credit card fraud also increased at the same time. Prevention is better than detection. So the existing system prevented the credit card fraud by identifying fraud in the application of the Credit card. Due to the limitation of the existing system, this...
Spatial data mining is a new research direction in the field of Data Mining. In recent years, with the continuous development of data mining technology, spatial data attracts more and more attentions of scholars and experts. Spatial clustering analysis is an important part of spatial data mining. Nowadays, spatial clustering analysis has become more and more mature, widely used in various fields....
In the Data mining process the dispensation of in sequence can be done with dissimilar ideas. An optimization is the dilemma of penetrating out the paramount announcement from all the practical solutions. Artificial Bee Colony (ABC) is proposed which is based on intelligent advancement and is defined to get the powerful and reliable solution. Process scheduling algorithm is presented under distributed...
The challenge to choose the best algorithm and its best parameters for a given problem is known as Combined Algorithm Selection and Hyperparameter Optimization Problem. Among all the classification algorithms available are those based on human comprehensible representations, such as decision trees and classification rule induction. These algorithms are usually chosen by the clarity of the results...
Automatic Manifold identification is currently a challenging problem in Machine Learning. This process consists on separating a dataset blindly, according to the form defined by the data instances in the space. Data are discriminated in groups defined by their form. These approaches are usually focused on continuity-based methods where the manifold follows a continuity criterion. Currently, clustering...
With the advent of the computer science, the data volume that needed to be processed under many practical situations increases dramatically, challenging many traditional machine learning techniques. Bearing this in mind, we made an intensive study on the optimization of decision tree algorithm and its corresponding porting to the big data analysis in this paper. An optimized genetic algorithm is merged...
As the rapid growth of information technology, variants of data mining techniques have been widely proposed to min the implicit or potential information. The discovered information may, however, consist of the confidential or sensitive information, which should be hidden before it is published or shared in the public place. Privacy-preserving data mining (PPDM) has been presented to hide the sensitive...
Inappropriate diagnosis of mental health illnesses leads to wrong treatment and causes irreversible deterioration in the client's mental health status including hospitalization and/or premature death. About 12 million patients are misdiagnosed annually in US. In this paper, a novel study introduces a semi-automated system that aids in preliminary diagnosis of the psychological disorder patient. This...
In the data mining research area, discovering frequent item sets is an important issue and key factor for mining association rules. For large datasets, a huge amount of frequent patterns are generated for a low support value, which is a major challenge in frequent pattern mining tasks. A Maximal frequent pattern mining task helps to resolve this problem since a maximal frequent pattern contains information...
Data mining is a knowledge discovery process which deals with the broad process of finding knowledge in data analyzing large storage of data in order to identify the relevant data. It is a powerful tool to uncover relationships within the data. Business intelligence (BI), is a distinctive term that refers to a lot of software applications, it is normally used to investigate a company's raw data for...
In this paper, we design a medical assistance system, which uses a data mining algorithm named improved genetic algorithm (IGA) to extract the proper behavior model according to the target database of the hospital, and develop the clinical pathways based on the behavior model. The quality of the behavior model can be evaluated by the application of the clinical pathways in the clinical diagnosis and...
This paper provides a algorithm, which is based on that kernelized fuzzy C-means uses on the study of source code mining, to solve the problem that the large number of quantities, multiple attributes and most of them discrete of software engineering. By using this algorithm, we can improve the efficiency of mining and seek faster and more effective cluster approaches. Meanwhile, we can also solve...
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