Comments by expert
... determined a discriminate function that separates occurrences of thrombosis with very low false negatives. However, ..... is it possible to translate the meaning and make us understood ? Weightening? ...
... determined a discriminate function that separates occurrences of thrombosis with very low false negatives. However, ..... is it possible to translate the meaning and make us understood ? Weightening? ...
A Novel K-Means Based Clustering Algorithm for High Dimensional
... so we replaced them with 0. On the other hand we need to calculate length of each vector base on its dimensions for further process. All attributes value in this table is ordinal and we arranged them with value from 1 to 5, therefore normalizing has not been done. There is not any correlation among ...
... so we replaced them with 0. On the other hand we need to calculate length of each vector base on its dimensions for further process. All attributes value in this table is ordinal and we arranged them with value from 1 to 5, therefore normalizing has not been done. There is not any correlation among ...
CPSC 6127 - Zanev - Columbus State University
... Attendance at all classes and other activities (lecture periods, laboratory sessions, tests, examinations, or other schedule meetings is required of every student at Columbus State University. The attendance record begins with the first meeting of the class, and one who registers late is responsible ...
... Attendance at all classes and other activities (lecture periods, laboratory sessions, tests, examinations, or other schedule meetings is required of every student at Columbus State University. The attendance record begins with the first meeting of the class, and one who registers late is responsible ...
Data Mining: Analysis of student database using Classification
... Data mining has been widely applied in the higher education field as private arts and science colleges, Engineering Colleges, Polytechnic Colleges and universities provide huge amount of data. Some of the application is to study features that affect student retention through monitoring the academic ...
... Data mining has been widely applied in the higher education field as private arts and science colleges, Engineering Colleges, Polytechnic Colleges and universities provide huge amount of data. Some of the application is to study features that affect student retention through monitoring the academic ...
Computer Applications
... The most popular tool used when mining is artificial intelligence (AI). AI technologies try to work the way the human brain works, by making intelligent guesses, learning by example, and using deductive reasoning. Some of the more popular AI methods used in data mining include neural networks, clust ...
... The most popular tool used when mining is artificial intelligence (AI). AI technologies try to work the way the human brain works, by making intelligent guesses, learning by example, and using deductive reasoning. Some of the more popular AI methods used in data mining include neural networks, clust ...
15: Outlier Mining in Data Streams Using Massive Online Analysis
... [16,17,18,19,20,21] is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed in such a way that it can handle the challenging problems of data streams. The state of the art algorithms are implemented in the framewor ...
... [16,17,18,19,20,21] is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed in such a way that it can handle the challenging problems of data streams. The state of the art algorithms are implemented in the framewor ...
Mining Industrial Logs for System Level Insights
... reliability of the entire system, a system-level condition monitoring is required [Ei15]. The processing of multiple sensor data, however, quickly reaches a complexity that exceeds computational tractability. To still maintain a system-wide view, the data from multiple sources must be reduced in vol ...
... reliability of the entire system, a system-level condition monitoring is required [Ei15]. The processing of multiple sensor data, however, quickly reaches a complexity that exceeds computational tractability. To still maintain a system-wide view, the data from multiple sources must be reduced in vol ...
Cover Slide Title
... implementing backend design, a designer or a methodology owner needs to understand the patterns seen in the design. Data like number of paths dominated by low leakage, Slope profile for cells with margin > x ps, Drive strength profile of cells in timing path, etc., are critical to make design decisi ...
... implementing backend design, a designer or a methodology owner needs to understand the patterns seen in the design. Data like number of paths dominated by low leakage, Slope profile for cells with margin > x ps, Drive strength profile of cells in timing path, etc., are critical to make design decisi ...
Over the past years… Big Data Big Data What is Big Data?
... http://research.larc.smu.edu.sg/palanteer/ http://research.larc.smu.edu.sg/palanteert/ ...
... http://research.larc.smu.edu.sg/palanteer/ http://research.larc.smu.edu.sg/palanteert/ ...
Data preprocessing using a priori knowledge
... We can see that PE2s freely use the platform : the number of groups shared only by them is significantly greater than the number of groups shared with trainers (668 vs. 292). However, we find that the activity is much higher in groups shared with trainers than in groups shared only by PE2s in produc ...
... We can see that PE2s freely use the platform : the number of groups shared only by them is significantly greater than the number of groups shared with trainers (668 vs. 292). However, we find that the activity is much higher in groups shared with trainers than in groups shared only by PE2s in produc ...
Running Resilient Distributed Datasets Using DBSCAN on
... This paper presents a new algorithm based on DBSCAN using the Resilient Distributed Datasets approach. This paper presents a parallel DBSCAN algorithm on top of Apache Spark. Many sets of data that need clustering cannot be adequately represented by just two dimensions. Improving the partitioning sc ...
... This paper presents a new algorithm based on DBSCAN using the Resilient Distributed Datasets approach. This paper presents a parallel DBSCAN algorithm on top of Apache Spark. Many sets of data that need clustering cannot be adequately represented by just two dimensions. Improving the partitioning sc ...
Prediction of Heart Disease using Classification Algorithms
... mining methods in predicting models in the domain of cardiovascular diagnoses. The experiments were carried out using classification algorithms Naïve Bayes, Decision Tree, K-NN and Neural Network and results proves that Naïve Bayes technique outperformed other used techniques [8]. The researchers [9 ...
... mining methods in predicting models in the domain of cardiovascular diagnoses. The experiments were carried out using classification algorithms Naïve Bayes, Decision Tree, K-NN and Neural Network and results proves that Naïve Bayes technique outperformed other used techniques [8]. The researchers [9 ...
Data mining - delab-auth
... – Multiple/integrated functions and mining at multiple levels Data to be mined – Database data (extended-relational, object-oriented, heterogeneous, legacy), data warehouse, transactional data, stream, spatiotemporal, timeseries, sequence, text and web, multi-media, graphs & social and information n ...
... – Multiple/integrated functions and mining at multiple levels Data to be mined – Database data (extended-relational, object-oriented, heterogeneous, legacy), data warehouse, transactional data, stream, spatiotemporal, timeseries, sequence, text and web, multi-media, graphs & social and information n ...
AL-ISRA UNIVERSITY Faculty of Administrative and Financial
... d. Web Mining 8. Challenges in Data Mining a. Scaling DM algorithms b. Extending to new data typ c. Distrubuted DM algorithms d. Large Scale Optimization of DM algorithims Ease of use of DM methods e. Privacy/Security issues Course objectives After completing the course, students will be able to: 1. ...
... d. Web Mining 8. Challenges in Data Mining a. Scaling DM algorithms b. Extending to new data typ c. Distrubuted DM algorithms d. Large Scale Optimization of DM algorithims Ease of use of DM methods e. Privacy/Security issues Course objectives After completing the course, students will be able to: 1. ...
Lecture-2-Data-Preprocessing-Part-1
... re,here's,hers,herself,him,himself,his,how,how's,i,i'd,i'll,i'm,i've,if,in ,into,is,isn't,it,it's,its,itself,let's,me,more,most,mustn't,my,myself,no, nor,not,of,off,on,once,only,or,other,ought,our,ours,ourselves,out,over,own ,same,shan't,she,she'd,she'll,she's,should,shouldn't,so,some,such,than,tha ...
... re,here's,hers,herself,him,himself,his,how,how's,i,i'd,i'll,i'm,i've,if,in ,into,is,isn't,it,it's,its,itself,let's,me,more,most,mustn't,my,myself,no, nor,not,of,off,on,once,only,or,other,ought,our,ours,ourselves,out,over,own ,same,shan't,she,she'd,she'll,she's,should,shouldn't,so,some,such,than,tha ...
Nonlinear dimensionality reduction
High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lie on an embedded non-linear manifold within the higher-dimensional space. If the manifold is of low enough dimension, the data can be visualised in the low-dimensional space.Below is a summary of some of the important algorithms from the history of manifold learning and nonlinear dimensionality reduction (NLDR). Many of these non-linear dimensionality reduction methods are related to the linear methods listed below. Non-linear methods can be broadly classified into two groups: those that provide a mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa), and those that just give a visualisation. In the context of machine learning, mapping methods may be viewed as a preliminary feature extraction step, after which pattern recognition algorithms are applied. Typically those that just give a visualisation are based on proximity data – that is, distance measurements.