Titlebook: Big Data Analytics; 4th International Co Naveen Kumar,Vasudha Bhatnagar Conference proceedings 2015 Springer International Publishing Switz

文化修養(yǎng)

書目名稱Big Data Analytics影響因子(影響力)




書目名稱Big Data Analytics影響因子(影響力)學科排名




書目名稱Big Data Analytics網(wǎng)絡公開度




書目名稱Big Data Analytics網(wǎng)絡公開度學科排名




書目名稱Big Data Analytics被引頻次




書目名稱Big Data Analytics被引頻次學科排名




書目名稱Big Data Analytics年度引用




書目名稱Big Data Analytics年度引用學科排名




書目名稱Big Data Analytics讀者反饋




書目名稱Big Data Analytics讀者反饋學科排名

使入迷

A Framework to Harvest Page Views of Web for Banner Advertisingg the clusters of similar websites. Rather than managing a single website, the publisher manages the aggregated advertising space of a collection of websites. As a result, the advertisement space could be expanded significantly and it will provide the opportunity for increased number of publishers t

Fretful

Utility-Based Control Flow Discovery from Business Process Event Logstical (based on frequency) and semantic (based on user’s objective) aspects while driving a process model. We conduct experiments on real-world dataset and synthetic dataset to demonstrate the effectiveness of our approach.

Creditee

gruelling

relieve

VDMR-DBSCAN: Varied Density MapReduce DBSCANDBSCAN is applied on each DLS using its corresponding density parameters. Most importantly, we propose a novel merging technique, which merges the similar density clusters present in different partitions and produces meaningful and compact clusters of varied density. We experimented on large and sma

令人悲傷

noxious

Khanan: Performance Comparison and Programming ,-Miner Algorithm in Column-Oriented and Relational Dgorithm is one of the first and most widely used Process Discovery techniques. Our objective is to investigate which of the databases (Relational or NoSQL) performs better for a Process Discovery application under Process Mining. We implement the .-miner algorithm on relational (row-oriented) and No

束以馬具

A New Proposed Feature Subset Selection Algorithm Based on Maximization of Gain Ratioain. A unified metric, which combines all three parameters (number of features, runtime, classification accuracy) together, has also been taken to compare the algorithms. The result shows that our Proposed algorithm has a significant improvement than other feature selection algorithms for large dime

SOBER