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Sequential pattern mining is a data mining technique that aims to extract and analyze frequent subsequences from sequences of events or items with time constraint. Sequence data mining was introduced in 1995 with the well-known Apriori algorithm. The algorithm studied the transactions through time, in order to extract frequent patterns from the sequences of products related to a customer. Later, this...
Roadway traffic safety is a major concern for transportation governing agencies as well as ordinary citizens. In order to give safe driving suggestions, careful analysis of roadway traffic data is critical to find out variables that are closely related to fatal accidents. In this paper we apply statistics analysis and data mining algorithms on the FARS Fatal Accident dataset as an attempt to address...
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,...
Leptospirosis is a disease that affects mainly low-income populations, with an incidence of 500,000 cases per year worldwide[1]. The disease has symptoms often confused with other febrile syndromes, such as dengue, influenza and viral hepatitis. Improved diagnosis of patients with leptospirosis is very important for health professionals, epidemiological surveillance and primarily for rapid evaluation...
The main problems and features of data mining are observed. The conveniences providing by the usage of functional language PROLOG for the solving of typical data mining problems are observed through the example of the realization of Naive Bayes algorithm.
With the rapid development of Internet, online survey becomes an emerging industry. It is a very challenging task to get interesting knowledge from the large-scale behavioral data of respondents. This paper firstly makes reduction of user properties and behavior data from an online survey company, and based on which we construct an online survey user model, then, an improved generalized sequential...
Data stored in educational database is increasing day by day. Data mining algorithms can be used to find hidden patterns from the student's database. These patterns can be used to find academic performance of students. The main aim of this study was to determine factors that influence the student's performance. This paper proposes Generalized Sequential Pattern mining algorithm for finding frequent...
In recent years, researches on smart phone services have received a lot of attention in both of the industry and academia due to a wide range of potential applications. Among them, one of popular topics is the mining and prediction of mobile application usage behaviors. In this paper, we propose a location-based approach to predict the mobile application usage behaviors. In this approach, we first...
Advanced satellite tracking technologies have collected huge amounts of wild bird migration data. Biologists use these data to understand dynamic migration patterns, study correlations between habitats, and predict global spreading trends of avian influenza. The research discussed here transforms the biological problem into a machine learning problem by converting wild bird migratory paths into graphs...
For IT Infrastructure Support (ITIS), it is crucial to identify opportunities for reducing service costs and improving service quality. We focus on streamlining service levels i.e., finding right resolution level for each ticket, to reduce time, efforts and cost for ticket handling, without affecting workloads and user satisfaction. We formalize this problem and present two statistics-based search...
In continuous Speech Recognition, for the problem of recognizing speaker's voice completely and accurately is difficult, and could not understand the meaning even if the machine could identify the voice completely. The improved algorithm is proposed innovatively based on association logic Apriori algorithm. The algorithm divide database into correlated partitions and locate the voice condition in...
Data preprocessing plays an important and critical role in the data mining process. Data preprocessing is required in order to improve the efficiency of an algorithm. This paper focuses on missing value estimation and prediction of time series data based on the historical values. A number of algorithms have been developed to solve this problem, but they have several limitations. Most existing algorithms...
In recent years, data mining technology is more and more widely used with the rapid development of network technology and database technique. Moreover, the data mining technology has been the research emphasis of experts and scholars in various kind of field, especially the hot pot of artificial intelligence. The application functions of data mining technology is rich in: classification analysis,...
In this paper, we present a Failure Prediction System (FPS) using a novel algorithm that extracts frequent anomalous behaviors based on multi-scale trend analysis of multiple network parameters. The proposed Correlation Analysis Across Parameters algorithm (CAAP) utilizes multiple levels of timescale analysis to reveal the frequent anomalous behaviors. The CAAP philosophy is that failures usually...
In this paper, we present a novel algorithm that extracts frequent anomalous behaviors based on multi-scale trend analysis of individual network parameters. The proposed Frequent Anomalous Behavior Mining (FABM) algorithm utilizes multiple levels of time-scale analysis to reveal the frequent anomalous behaviors. This makes the proposed algorithm robust to unreliable, redundant, incomplete and contradictory...
To gain the competitive advantage in today's age of technology, growing data and to bear the competitive pressure, making strong decisions according to customer's need and market trend has become very important. With huge amount of data on internet, web data mining has become very significant. Web Usage helps companies to produce productive information pertaining to the future of their business function...
Existing trajectory prediction algorithms mainly employ kinematical models to approximate real world routes and always ignore spatial and temporal distance. In order to overcome the drawbacks of existing trajectory prediction approaches, this paper proposes a novel trajectory prediction algorithm. It works as: (1) mining the interesting regions from trajectory data sets; (2) extracting the trajectory...
For Enterprise, how to discover the useful data, fresh knowledge and information in order to help decision makers quickly and accurately from the data ocean is an important question, this gives theorists and practitioners' new research direction. SJEP simulation algorithm which based on the logistics information platform can help maker decisions quickly and accurately to achieve the control of the...
This paper introduces improving rate and proposes the incremental mining algorithm with the weighted model for optimizing association rules based on CBA mining algorithm. The risk analysis of the strong association rules is proposed for trend forecasting. And the risk degree of the lost rules based on the incremental mining is also analyzed. Comparing with the traditional algorithm, the improved algorithm...
Acquiring new customers in any business is much more expensive than trying to keep the existing ones. Many churn management models have been developed over the years, mostly focusing on prediction accuracy and not considering the range of parameters and processes essential to manage the system as a whole. This study presents CMF (churn management framework), a framework which tries to covers most...
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