> for (i in 1:round(sqrt(dim(traindata)[1]))){ + model <- knn(train = traindata[,-1], test = testdata[,-1], + cl = traindata$PO, k = i) + Freq <- table(testdata[,1], model) + print(1-sum(diag(Freq))/sum(Freq)) + } [1] 0.4117647 [1] 0.4705882 [1] 0.32352...
Data mining: An overview from a database perspective. Provides information on data mining techniques developed in several research communities. Classification of available databases to be mined; Requirements a... Chen,Ming-Syan,Han,... - 《IEEE Transactions on Knowledge & Data Engineering》 被引量...
In this paper we propose a new modified tree for classification in Data Mining. The proposed modified Tree is inherited from the concept of the decision tree and knapsack problem. A very high dimensional data may be handled with the proposed tree and optimized classes may be generated. 展开 ...
Data mining on clinical data is a challenging area in the field of medical research, aiming at predicting and discovering patterns of disease occurrence and prognosis based on detected symptoms and reported health conditions. Data mining is the process of recovering related, significant and imperative...
This paper analyzes readers' borrowing records using the techniques of data analysis, building a data warehouse, and data mining. Findings – The paper finds that after mining data, readers can be classified into different groups according to the publications in which they are interested. Some ...
Data volumes have increased noticeably in the few passed years and also expected to have consistent growth in coming years. Data mining is the most promising solution for dealing with such huge amount of data with little knowledge for its categorization as it helps in organizing data into sensible...
The Application of Data Mining Technology to Build a Forecasting Model for Classification of Road Traffic Accidents. With the ever-increasing number of vehicles on the road, traffic accidents have also increased, resulting in the loss of lives and properties, as well as i... Shiau,Yau-Ren,Tsai...
Statistical data-mining (DM) and machine learning (ML) are promising tools to assist in the analysis of complex dataset. In recent decades, in the precision of agricultural development, plant phenomics study is crucial for high-throughput phenotypin
of data; advances in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets; methods for the extraction of knowledge from data, and applications of advanced methods in specific domains of practice. Articles illustrate how new domain...
Recovering the missing components in a large noisy low-rank matrix: application to SFM In computer vision, it is common to require operations on matrices with "missing data," for example, because of occlusion or tracking failures in the Struc... P Chen,D Suter - 《IEEE Transactions on ...