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Frequent itemset mining is the technique used mostly in field of data mining like finance, health care system. We are focusing on methodologies for extracting the useful knowledge from given data by using frequent itemset mining. Most important use of FIM is customer segmentation in marketing, shopping cart analyzes, management relationship, web usage mining, and player tracking and so on. Association...
In this paper, the state-of-the-art of big data is reviewed. Challenges, opportunities and tools will be discussed. Some emerging technologies will be looked to promote big data applications. The applications of big data in smart grid in some countries will be summarized too.
With the development of digital cable interactive business and the diversification of the customers' demand, grouping TV programmes based on preferences of users effectively is vital for market segmentation and differentiation. The study summarizes the main principle and characteristic of clustering algorithm, and uses K-Means algorithm to show TV programmes preference grouping based on 52392 subscribers...
We all know that the information passed through internet is in terms of packets. The alerts produced by all the existing intrusion detection systems are false alerts which can cause to decrease the efficiency and the accuracy is also low. The alerts generated by all the existing intrusion detection systems are isolated alerts and they will focuses on low-level attacks. So in this research paper diverse...
Agent framework such as Aglets has crosscutting concern (CCC) that is legacy from object oriented (OO) programming. Refactoring is needed to make a clean agent framework from the problems. Aspect mining and aspect identification is an important process that has to be conducted in refactoring process. Aspect implementation can be conducted after aspect mining have been succeeded to identify CCC. This...
The vigorous growth of big data has triggered both opportunities and challenges in business and industry. However, Web big data distributed in diverse sources with multiple data structures frequently conflict with each other, i.e. inconsistency in cross-source Web big data. In this paper, we propose a state-of-the-art architecture of auto-discovering inconsistency with Web big data. Our contributions...
In this paper, a new technique is presented for mining key domain areas from scientific publications. A domain refers to a particular branch of scientific knowledge and hence largely defines the theme of any scientific research paper. The proposed technique stems from a fusion of knowledge derived from natural language processing and machine learning. Some words or phrases are extracted based on their...
Exploring social events from Social Network Services (SNSs) (known as detecting events) has been studied in many researches because of its challenges. Most of researches focus on detecting events based on textual context. In this paper, we propose a novel framework using media data for not only systematically identifying events but also ranking these events. Firstly, we detect events from the photos...
Big data has been an important issue in many research domains. The amount of data generated is not only growing in the developed world, also the developing countries is experiencing rapid growth in data generation. However, a large part of the data generated in the developing countries has a different origin than in the rest of the world: the developing world is progressing rapidly to the mobile era...
K-means is one of the most significant clustering algorithms in data mining. It performs well in many cases, especially in the massive data sets. However, the result of clustering by K-means largely depends upon the initial centers, which makes K-means difficult to reach global optimum. In this paper, we developed a novel algorithm based on finding density peaks to optimize the initial centers for...
Since past few years there is tremendous advancement in electronic commerce technology, and the use of credit cards has increased dramatically. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In this paper the authors present the underlying theory of a hybrid model of an Intelligent Fraudulent Detection...
The user enters any query to find desired information. To discover number of user search goals and representing each goal with some keyword, we first infer user search goals for a query by clustering feedback sessions. For that, we use a concept of pseudo document, which is the revised version of feedback session. Then the user search goals are determined by clustering the pseudo documents and it...
There are so many existing algorithms proposed that mines frequent patterns from certain or precise data. But now a days demand of uncertain data mining is increasing. There are many situations in which data are uncertain. There are mainly two approaches for mining uncertain data like level-wise approach and pattern-growth approach. Level-wise approach uses generate and test framework so it requires...
The concept behind this particular aspect lies on the fact to determine and customize the simplicity and the most basic scenario. The basicity lies on the fact that we have been using the concept of Data Mining and even the algorithms are included that merely includes the efficiency of NIDS that is Network Intrusion Detection System. We have seen a lot of aspects and different concepts being used...
Data mining is one of the most exciting fields of research for the researcher. As data is getting digitized, systems are getting connected and integrated, scope of data generation and analytics has increased exponentially. Today, most of the systems generate non-stationary data of huge, size, volume, occurrence speed, fast changing etc. these kinds of data are called data streams. One of the most...
Multiclass classification is the task of classifying the samples into more than two classes. Generally multi-classifiers face difficulty in classifying samples those are very close to the separating hyperplane, known as Generalization error. Generalization error can be reduced by maximizing the margin of the separating hyperplanes. Support Vector Machine (SVM) is a maximum-margin classifier, its aim...
In this paper, we propose a hybrid method for intrusion detection which is based on k-means, naive-bayes and back propagation neural network (KBB). Initially we apply k-means which is partition-based, unsupervised cluster analysis method. In the form of clusters, we attain the gathered data which can be easily processed and learned by any machine learning algorithm. These outcomes are provided to...
Social emotion analysis of online users has become an important task for mining public opinions, which aims at detecting the readers' emotions evoked by online news articles. In this paper, we focus on building a social emotion analysis system (SEAS) for online news. The system has implemented a text data crawler for mainstream online news websites, the modules of document preprocessing, document...
This paper presents a computing device-based mechanism for checking patient service information. Patients who use this system can check their information about their activities during a hospital stay including what he is supposed to do now, where he has to go now, how many wait queues are left, the total fee he is going to pay, and so on. The information shown to patients is personalized and is extracted,...
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...
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