scholarly journals Research on Intelligent Tourism Information System Based on Data Mining Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jia Du
Keyword(s):  
Information System ◽  
Big Data ◽  
Information Needs ◽  
Data Mining Algorithm ◽  
Distributed Environment ◽  
Big Data Visualization ◽  
Tourism Information ◽  
Visualization Technology ◽  
It Application ◽  
Smart Tourism

Smart tourism purposes symbolize a new idea of IT application to increased competition and satisfaction of all stakeholders, including visitors as co-creators of tourism products and co-promoters of a destination. To improve the effect of smart tourism, this paper improves the common big data technology through algorithm enhancement to improve the intuitive effect of big data. We construct big data visualization technology and realize real-time online visualization of tourism data. In the spark-distributed environment, we use the conventional K clustering technique to improve the final output utilizing clustering means. The research results show that the smart tourism information system based on big data constructed in this paper can meet actual tourism information needs and user experience needs. The outcomes of the experimental results show that the proposed predictor significantly outperforms based on the improved algorithm.

Issues of K Means Clustering While Migrating to Map Reduce Paradigm with Big Data: A Survey

2016 ◽  
Vol 6 (6) ◽  
pp. 3047 ◽  
Author(s):  
Khyati R Nirmal ◽  
K.V.V. Satyanarayana
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Distinct Group ◽  
Map Reduce ◽  
Data Mining Algorithm ◽  
Distributed Environment ◽  
Significant Information ◽  
User Influence ◽  
Initial Cluster ◽  
Machine Learning Approach

<p><span>In recent times Big Data Analysis are imminent as essential area in the field of Computer Science. Taking out of significant information from Big Data by separating the data in to distinct group is crucial task and it is beyond the scope of commonly used personal machine. It is necessary to adopt the distributed environment similar to map reduce paradigm and migrate the data mining algorithm using it. In Data Mining the partition based K Means Clustering is one of the broadly used algorithms for grouping data according to the degree of similarities between data. It requires the number of K and initial centroid of cluster as input. By surveying the parameters preferred by algorithm or opted by user influence the functionality of Algorithm. It is the necessity to migrate the K means Clustering on MapReduce and predicts the value of k using machine learning approach. For selecting the initial cluster the efficient method is to be devised and united with it. This paper is comprised the survey of several methods for predicting the value of K in K means Clustering and also contains the survey of different methodologies to find out initial center of the cluster. Along with initial value of k and initial centroid selection the objective of proposed work is to compact with analysis of categorical data.</span></p>

scholarly journals Issues of K Means Clustering While Migrating to Map Reduce Paradigm with Big Data: A Survey

2016 ◽  
Vol 6 (6) ◽  
pp. 3047
Author(s):  
Khyati R Nirmal ◽  
K.V.V. Satyanarayana
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Distinct Group ◽  
Map Reduce ◽  
Data Mining Algorithm ◽  
Distributed Environment ◽  
Significant Information ◽  
User Influence ◽  
Initial Cluster ◽  
Machine Learning Approach

<p><span>In recent times Big Data Analysis are imminent as essential area in the field of Computer Science. Taking out of significant information from Big Data by separating the data in to distinct group is crucial task and it is beyond the scope of commonly used personal machine. It is necessary to adopt the distributed environment similar to map reduce paradigm and migrate the data mining algorithm using it. In Data Mining the partition based K Means Clustering is one of the broadly used algorithms for grouping data according to the degree of similarities between data. It requires the number of K and initial centroid of cluster as input. By surveying the parameters preferred by algorithm or opted by user influence the functionality of Algorithm. It is the necessity to migrate the K means Clustering on MapReduce and predicts the value of k using machine learning approach. For selecting the initial cluster the efficient method is to be devised and united with it. This paper is comprised the survey of several methods for predicting the value of K in K means Clustering and also contains the survey of different methodologies to find out initial center of the cluster. Along with initial value of k and initial centroid selection the objective of proposed work is to compact with analysis of categorical data.</span></p>

Research and Application of Responsive Big Data Visualization Based on HTML5

2014 ◽  
Vol 631-632 ◽  
pp. 1075-1079
Author(s):  
Pei Yang ◽  
Hai Yun Han
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Mobile Platform ◽  
Technology Research ◽  
Mass Data ◽  
Volume Velocity ◽  
Big Data Visualization ◽  
Visualization Technology ◽  
Special Points ◽  
Meaningful Data

Research the responsive visualization technology of big data based on HTML5. Big data has 4 special points: volume, velocity, variety and value. Our purpose is to mine the value of big data with the visualization technology. There are many platforms such as desktop and mobile platform, and each kind of device may have different resolution, based on HTML5, CSS3 and JavaScript technology, research the responsive visualization technology to fit all platforms, then we can mine meaningful data of the mass data, guide the development of related forecasting and strategy.

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