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Many industries are being revolutionized through the use of advanced analytical tools that generate insights from large sets of data. These tools are used as a part of a diversely described but analogous set of pursuits, such as “data science,” “data mining,” and “big data.” In manufacturing, they result in improved quality, improved cost of manufacturing, and more streamlined approaches. Many of...
With the rapid development of science and technology and the growing popularity of computer networks, the scale of network users is gradually expanding, and the behavior of network users is becoming more and more complicated. A large number of studies show that the user's actual interest is closely related to the browsing behavior on the web page. Through the user browsing behavior analysis can obtain...
The blast furnace gas is an important secondary energy for the iron and steel production. Establishing an effective model to describe the state of BFG system is of great significant to maintain the system balance and stability. Considering the strong coupling characteristics of the blast furnace gas system and the high level noises in the industrial data, a simplex unscented Kalman filter-based Wang-Mendel...
Thanks to recent advance in massive social data and increasingly mature big data mining technologies, information diffusion and its control strategies have attracted much attention, which play pivotal roles in public opinion control, virus marketing as well as other social applications. In this paper, relying on social big data, we focus on the analysis and control of information diffusion. Specifically,...
Large data handling and analysis either on industrial level or on research level has always been facing problems. These problems increase with the increase in machine dedicated software packages. Large data processing and analysis is prone to errors and is time consuming while moving data from data generation to data analysis. In this paper, first-methods of data generation, methods to move data from...
Process mining research discipline offers a spectrum of techniques for analysing event logs. Event logs represent the history of process execution. This information can be used for monitoring, analysing and improving the operational processes. The currently available methods in process mining emphasise on constructing the static process model. These models depict various dimensions of the process...
In recent years, online social networks have gained tremendous popularity because of the massive number of online users, the fast spread of information, and strong inter-personal influence. However, due to the high complexity of the user interaction and the real-time changing of the online social networks, it is still a big challenge to model the spreading process of the information delicately and,...
This paper presents a data mining technique for qualitative analysis of Hodgkin-Huxley model of cell excitability. Such problem cannot be solved analytically. Therefore we apply Monte-Carlo techniques for the generation of model parameters, and use data mining algorithm for classification of learning tuples obtained. As a result we attain a decision tree capable of classifying the excitability depending...
Discrimination discovery and prevention has received intensive attention recently. Discrimination generally refers to an unjustified distinction of individuals based on their membership, or perceived membership, in a certain group, and often occurs when the group is treated less favorably than others. However, existing discrimination discovery and prevention approaches are often limited to examining...
When a data holder wants to share databases that contain personal attributes, individual privacy needs to be considered. Existing anonymization techniques, such as l-diversity, remove identifiers and generalize quasi-identifiers (QIDs) from the database to ensure that adversaries cannot specify each individual's sensitive attributes. Usually, the database is anonymized based on one-size-fits-all measures...
The paper presents a data mining technique for qualitative analysis of functional differential equations of compartmental type. As a result we get the decision tree able to classify the system behaviour depending on relations between initial conditions and between rate constants. Antitumour immunity example is presented.
This study addressed the literature gap, identified by other researchers, that there are too few examples of applied empirical open big data analytics. Using correspondence analysis as a big data analytical technique, this study demonstrates how qualitative big data type could be analyzed to identify hidden factor relationships that may assist strategic decision making. We use a 64MB open meta big...
Basic technologies for creation of predicative analytical core for industrial systems of decision-making processes supporting are considered. It is proposed to use architectural solutions based on international standards of Data Mining. Issues of mobility and computational efficiency of analytical technologies for Big Data are discussed.
Many location based services, such as FourSquare, Yelp, TripAdvisor, Google Places, etc., allow users to compose reviews or tips on points of interest (POIs), each having a geographical coordinates. These services have accumulated a large amount of such geo-tagged review data, which allows deep analysis of user preferences in POIs. This paper studies two types of user preferences to POIs: topical-region...
Regression analysis is one of the components of data mining techniques. Various regression algorithms have been proposed to mine the data efficiently and to propose a suitable business model. Every algorithm caters to a particular need and not necessarily produces the best fit futuristic model for all types of data. On the other hand, todays Web is expanding rapidly and affecting all aspects of our...
This paper raises a Microblog topic detection method based on text clustering and topic model analysis. It solves the problem that the traditional topic detection method is mainly applicable for traditional media text, which is not very effective in handling sparse Micro blog short texts. In consequence of the structural data of the Microblog, which exists rich inter-textual contextual information...
Most existing topic models focus either on extracting static topic-sentiment conjunctions or topic-wise evolution over time leaving out topic-sentiment dynamics and missing the opportunity to provide a more in-depth analysis of textual data. In this paper, we propose an LDA-based topic model for analyzing topic-sentiment evolution over time by modeling time jointly with topics and sentiments. We derive...
The goal of feature extraction in multimedia mining is to discover important features for represented into a form that can represent information of multimedia data. Sequential pattern is one form of data representation formed of a number of elements that appear in sequence. The goal of this study is to analyze sequential pattern representation performancy to improve accuracy and efficiency. Analysis...
Spatio-Temporal data is related to many of the issues around us such as satellite images, weather maps, transportation systems and so on. Furthermore, this information is commonly not static and can change over the time. Therefore the nature of this kind of data are huge, analysing data is a complex task. This research aims to propose an intermediate data model that can represented suitable for Spatio-Temporal...
In order to provide marketing support for online shop owner, this paper describes how to analyze historical data of online shop customer behaviors by data mining, and establishes logistic predictive modeling by SAS, then predicts whether customers purchase a Tablet PC online shop. Practice has proved that data mining techniques and predictive regression modeling can be effectively integrated in the...
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