Classification Rules For Data Research Articles

An Earthquake Emergency Web Data Cleaning and Classification Method Based on Word Frequency and Position Weighting.

The speed of earthquake emergency web document data cleaning is one of the key factors affecting emergency rescue decision-making. Data classification is the core process of data cleaning, and the efficiency of data classification determines the speed of data cleaning. This article is based on earthquake emergency Web document data and HTML structural features, combined with TF-IDF Algorithm and information calculation model, improves the word frequency factor and location factor parameters, and proposes the weighted frequency algorithm P-TF-IDF for earthquake emergency Web documents. To filter out less frequent words and optimize the FastText model, N-gram Feature word vectors effectively improve the efficiency of Web document data classification; for text classification data, use missing data recognition rules, data classification rules, and data repair rules to design an artificial intelligence-based earthquake emergency network information data cleaning framework to detect invalid data sets value, complete data comparison and redundancy judgment, clean up data conflicts and data errors, and generate a complete data set without duplication. The data cleaning framework not only completes the fusion of earthquake emergency network information but also provides a data foundation for the visualization of earthquake emergency data.

Design of Online Education Information Management System Based on Data Mining Algorithm

Online education is the core field in China now, and many online education institutions have reached thousands or even thousands of teachers. A large amount of information has been gathered in the corresponding personnel management system modules and talent databases of the institution itself, such as personnel student status query management and performance evaluation analysis and management. How to reuse these data to transform existing data management into usable knowledge has become a problem that organizations should not underestimate. The purpose of this paper is to study the theoretical knowledge related to data mining algorithms, put forward the process and classification of data mining, and focus on the mining process and common methods of data classification rules. Experimental data shows that 31% of parents chose online education in 2019, compared with only 14% in 2017. Among them, the natural lack of offline education resources in third- and fourth-tier cities online education makes up for the lack of offline resources. The results show that third- and fourth-tier cities will become the driving force for the development of online education in the future.

Optimization of Decision Tree Machine Learning Strategy in Data Analysis

Aiming at the problem of too coarse matching of machine learning decision tree models in the field of data mining and low prediction accuracy, the corresponding improved optimization strategies are proposed. First, the field matching degree of data is further improved by discretizing continuous attributes in multiple intervals. Then, the method makes the selection of business attributes more reasonable in the downward splitting process of the model by compensating the weight of feature attributes by business sensitivity indicators. Finally, the data classification rule transformation is used to further improve the data prediction accuracy of the model. The experimental results show that the introduction of the tree model generated by the business sensitivity index is more concise. In addition, the business pertinence and data classification capabilities are stronger. The results show that the transformed and upgraded data classification rules can effectively improve the accuracy of data prediction compared with the traditional optimization algorithm.

Adaptive Genetic Algorithm Based Data Classification Rules Learning System

It’s a worthy research topic to use genetic algorithm for classification rules in data mining. In this paper, it was studied and researched in-depth. Firstly, we combined genetic algorithm and machine learning together, and then analyzed architecture of the genetic algorithm-based classification system, and also its development concrete structure was given. Secondly, we proposed a data classification rules learning system based on adaptive genetic algorithm, which can learn the classification rules accurately from the dataset. Finally the standard Play Tennis dataset was used for a closed test and after learning the system got three classification rules all with 100% accuracy rate, which fully demonstrated the feasibility of this algorithm.

Classification Rules under Autoregressive and General Circulant Covariance

We develop classification rules for data that have an autoregressive circulant covariance structure under the assumption of multivariate normality. We also develop classification rules assuming a general circulant covariance structure. The new classification rules are efficient in reducing the misclassification error rates when the number of observations is not large enough to estimate the unknown variance–covariance matrix. The proposed classification rules are demonstrated by simulation study for their validity and illustrated by a real data analysis for their use. Analyses of both simulated data and real data show the effectiveness of our new classification rules.

QUANTIFICATION OF UNCERTAINTY IN CLASSIFICATION RULES DISCOVERED FROM DATABASES

We apply rough set constructs to inductive learning from a database. A design guideline is suggested, which provides users the option to choose appropriate attributes, for the construction of data classification rules. Error probabilities for the resultant rule are derived. A classification rule can be further generalized using concept hierarchies. The condition for preventing overgeneralization is derived. Moreover, given a constraint, an algorithm for generating a rule with minimal error probability is proposed.

A Numerical Classification Method for Partitioning of a Large Multidimensional Mixed Data Set

Sometimes the problem of data classification arises without any knowledge of the underlying classes. Furthermore, not enough sample data from the classes may be available to develop reliable parametric estimates and, hence, data classification rules. However, when the size of the mixed data to be classified is large, computer aided techniques for displaying data with histograms and frequency tables can be exploited to compensate partially for the lack of class knowledge and sample data. Based on this concept, a numerical classification procedure is developed and is described in this paper. The classification rule is completely data directed and utilizes the data marginal distributions in a sequential manner. The technique was employed for partitioning certain remotely sensed multispectral scanner data for a number of sites in the U.S. and was found to be fairly competitive when compared to some commonly used parametric approaches utilizing training sample data.