The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
No Evidence of Disease (NED) is breast cancer patient condition status which it indicates that they can life, no find the cancer by tested, and without any symptoms of cancer in period of times, after they received primary treatment. NED is a critical status, because it involves the treatment type and patient cancer condition factors. This paper examines about breast cancer problem in data mining...
For a large sum of data collected and stored continually, it is more and more necessary to mine association rules from database, and the Apriori algorithm of association rules mining is the most classical algorithm of database mining and is widely used. However, Apriori algorithm has some disadvantages such as low efficiency of candidate item sets and scanning data frequently. Support and confidence...
With the advent of the big data era, data mining technology has gradually become mature, association rules analysis is also applied in many fields. Web log mining is an important way to do some personalized services and achieve Web personalize. Apriori algorithm is a classical algorithm of association rules, but it has a lot of shortcomings. In recent years, the improvement about Apriori algorithm...
Classification is the process of finding a model or function that describes and distinguishes data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown. The goal of classification is to accurately predict the target class for each case in the data. In sequence database having sequences, in which each sequence is a list of...
Datamining is the process of extracting interesting information of patterns from large databases. One of the most important datamining task and well-researched is the association rules mining. It aims to find the interesting correlation and relations among sets of items in the transaction databases. One of the main problems related to the discovery of these associations that a decision maker faces...
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'...
The grant for the research gives the researcher the important opportunity to make fruitful research results. Recently, the notification of the government grants and some Foundation grants in the various fields informs the researchers through Internet. However, the notification provided through Internet includes ambiguous and complex. The researchers will fail to notice the grant information which...
Bacterial colonies perform a cooperative distributed exploration of the environment. This paper describes bacterial colony networks and their skills to explore resources as a tool for mining association rules in databases. The proposed algorithm is designed to maintain diverse solutions to the problem at hand, and its performance is compared to other well-known bio-inspired algorithms, including a...
Sequential pattern mining is valuable approach to uncover consumer buying behaviour from huge sequence database. Weather prediction, web log analysis, stock market analysis, scientific research, sales analysis, and so on are the application of sequential pattern mining. The pattern that is recent and profitable can't discover by conventional sequential pattern mining. So, RFM-based sequential pattern...
Choosing and implementing technologies to extract value from big data are constant challenges for business and governments alike. This paper describes the design and implementation of a data mining tool to analyze the XML data of the U.S. university campus crimes. The main aim of this tool is to extract data stored in XML documents and to provide summarized information that can help students in determining...
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...
Educational data mining is emerging for useful knowledge hidden in educational databases. Frequent temporal pattern mining is one of the popular mining tasks to help us get insights into the characteristics of the students and further of their study. As time goes, educational databases in an academic credit system keep increasing and updated in nature. Thus, frequent temporal pattern mining in educational...
In the area of Data Mining, We generally use many techniques for data analysis, among them, association rule learning is a well-liked and well researched technique for discover the interesting relations among the variables in large databases. Association rules are a part of intelligent systems because all the intelligent systems are using the associations. Association rules are usually needed to satisfy...
Association rules mining is one of the most popular and significant issue in data mining and intends to discovery interest relations between variables in database. In our paper, we implemented an improved parallel Apriori algorithm which realized both count and candidate generation steps under MapReduce framework, while existing parallel Apriori algorithm only considered count step. We analyzed the...
Discovery of association rules is one of the very important tasks in data mining. So far Conventional Association Rule Mining (CARM) has proven its importance in medical, biology and business fields. As it is unable to extract time based association rules, it substantiated to unsuitable for intelligent transportation applications. The CARM extended to spatiotemporal processes, generating time based...
Unsupervised fuzzy c-means clustering (FCM) algorithm is applied to intrusion detection so that intrusion detection system can directly deal with unlabeled original network data. Because particle swarm optimization (PSO) algorithm is easy to implement global optimum, FCM algorithm is improved based on particle swarm algorithm, in order to address the deficiencies that FCM is easy to fall into local...
In this paper, we describe the basic concepts of multidimensional sets and multidimensional association rules, and propose a improved algorithm of association rule mining based on multidimensional sets. This algorithm can find out the maximal frequent item sets of each dimensional subset, and at the same time pruning the database, this can substantially reduce the workload of the subsequent mining...
Classical algorithms of keywords extraction can hardly get low computational complexity and high accuracy. The association rule mining based algorithm is proposed in this paper. This algorithm adopts improved FP-Growth algorithm to extract word co-occurrence information, utilizes the similarity algorithm to eliminate synonyms, and removes noisy words and simplified features of candidates, thus reducing...
The significant development in field of data collection and data storage technologies have provided transactional data to grow in data warehouses that reside in companies and public sector organizations. As the data is growing day by day, there has to be certain mechanism that could analyze such large volume of data. Data mining is a way of extracting the hidden predictive information from those data...
Associated rule mining has become a common subject in data mining research field that is very popular used for marketing basket analysis. The discovery knowledge pattern mined can provide insight to the data holder as well as be invaluable in important task, such as decision making and strategic planning. This paper presents an associated rule mining technique that significantly helping for improvement...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.