Research on Pattern Analysis and Data Classification Methodology for Data Mining and Knowledge Discovery

Abstract

A plethora of big data applications are emerging and being researched in the computer science community which require online classification and pattern recognition of huge data pools collected from sensor networks, image and video systems, online forum platforms, medical agencies etc. However, as an NP hard issue data mining techniques are facing with lots of difficulties. To deal with the hardship, we conduct research on the novel algorithm for data mining and knowledge discovery through network entropy. We firstly introduce necessary data analysis techniques such as support vector machine, neural network and decision tree methods. Later, we analyze the organizational structure of network graphical pattern with the knowledge of machine learning methodology and graph theory. Eventually, our modified method is finalized with decision and validation implementation. The simulation results of our approach on different databases show the feasibility and effectiveness of our proposed framework. As the final part, we provide our conclusion and prospect.

Assessing impact of problem-based learning using data mining to extract learning patterns

“…Decision tree (classification technique) is used to build prediction models, and identify important qualitative and quantitative attributes contributing to SAP (Jiang et al. , 2016).…”

A Framework to Study Data Mining to Predict Pesticide Uses

“…The algorithms are used as defined in WEKA [2], [6], [7], [12]. Some researchers use a combination technique with classification algorithms [1], [4], [5], [13], [16]. Performances of these algorithms are also compared to find the best classifier.…”