Data mining - Department of Computer Science and Engineering
... An Example Data: Loan application data Task: Predict whether a loan should be approved or not Performance measure: accuracy ...
... An Example Data: Loan application data Task: Predict whether a loan should be approved or not Performance measure: accuracy ...
PROGRAM Sixth Annual Winter Workshop: Data Mining, Statistical
... data that summarize behavior, and then analyze the resulting reduced information. While the study of summaries is important, and can sometimes suffice, it often does not succeed in exploiting fully the information in a very large database. Intensive learning is needed; a significant fraction of the ...
... data that summarize behavior, and then analyze the resulting reduced information. While the study of summaries is important, and can sometimes suffice, it often does not succeed in exploiting fully the information in a very large database. Intensive learning is needed; a significant fraction of the ...
10 5
... • One can take a random sample of ~1000 galaxies & invert that while bootstrapping n times from full sample • However, some low-rank matrix approximations work well such as Cholesky Decomposition, Subset of Regressors but can have numerical problems. • Solution: V-method (Cholesky decomposition with ...
... • One can take a random sample of ~1000 galaxies & invert that while bootstrapping n times from full sample • However, some low-rank matrix approximations work well such as Cholesky Decomposition, Subset of Regressors but can have numerical problems. • Solution: V-method (Cholesky decomposition with ...
Data Mining for Staff Recruitment in Education System using WEKA
... dedicated. This analysis is based on the recruitment of the teaching staff. In order to analyze the rules given for assistant professor and associate professor by University Grant Commission, this paper generates an application of data mining for staff recruitment in education system. This applicati ...
... dedicated. This analysis is based on the recruitment of the teaching staff. In order to analyze the rules given for assistant professor and associate professor by University Grant Commission, this paper generates an application of data mining for staff recruitment in education system. This applicati ...
Distributed approximate spectral clustering for large
... The rapidly declining cost of sensing technologies has led to proliferation of data in virtually all fields of science. Consequently, the sizes of datasets used in all aspects of datadriven decision making, inference, and information retrieval tasks have exponentially grown. For example, datasets for ...
... The rapidly declining cost of sensing technologies has led to proliferation of data in virtually all fields of science. Consequently, the sizes of datasets used in all aspects of datadriven decision making, inference, and information retrieval tasks have exponentially grown. For example, datasets for ...
Library Management System Using Association Rule Mining
... Sometimes clustering is performed not so much to keep records together as to make it easier to see when one record sticks out from the rest. III. PROPOSED WORK A. ...
... Sometimes clustering is performed not so much to keep records together as to make it easier to see when one record sticks out from the rest. III. PROPOSED WORK A. ...
A Method to Improve the Accuracy of K
... logical to assume that the value of characteristics weight is correct, so there is no reason to change the weights. On the other hand, when the classification is done correctly and the rate of the characteristics is the same, changing the weight of characteristics will have no effect on making decis ...
... logical to assume that the value of characteristics weight is correct, so there is no reason to change the weights. On the other hand, when the classification is done correctly and the rate of the characteristics is the same, changing the weight of characteristics will have no effect on making decis ...
Data warehousing and data mining
... • Data warehousing: process of consolidating data in a centralized location • Data mining: process of analyzing data to find useful patterns and relationships Dr. Sunita Sarawagi ...
... • Data warehousing: process of consolidating data in a centralized location • Data mining: process of analyzing data to find useful patterns and relationships Dr. Sunita Sarawagi ...
Anomaly Detection
... • Anomalies detected using clustering based methods can be: – Data records that do not fit into any cluster (residuals from clustering) – Small clusters – Low density clusters or local anomalies (far from other points within the same cluster) ...
... • Anomalies detected using clustering based methods can be: – Data records that do not fit into any cluster (residuals from clustering) – Small clusters – Low density clusters or local anomalies (far from other points within the same cluster) ...
Distributed mining first-order frequent patterns
... because the variable X occurs before variable Y in the literal p/2. By encoding reordered pattern we get the same code for all equivalent patterns. ...
... because the variable X occurs before variable Y in the literal p/2. By encoding reordered pattern we get the same code for all equivalent patterns. ...
SOM in data mining
... Clustering of data is one of the main applications of the Self-Organizing Map (SOM) [1]. U-matrix is one of the most commonly used methods to cluster the SOM visually. However, in order to be really useful, clustering needs to be an automated process. When clusters are identified visually the result ...
... Clustering of data is one of the main applications of the Self-Organizing Map (SOM) [1]. U-matrix is one of the most commonly used methods to cluster the SOM visually. However, in order to be really useful, clustering needs to be an automated process. When clusters are identified visually the result ...
Data Mining
... It guarantees a non-stratified sample because there is only one instance in the test set! ...
... It guarantees a non-stratified sample because there is only one instance in the test set! ...
Assignment for Data Mining Session on Learning Curves, March 27
... 6) Change the left axis back to Problem and change the top axis from Error Rate (%) to “Residual Error Rate % (Predicted – Actual)”. This variable shows the difference between the LFA model’s prediction and the actual data. Go to the Performance Profiler panel on left and change Sort By from Error R ...
... 6) Change the left axis back to Problem and change the top axis from Error Rate (%) to “Residual Error Rate % (Predicted – Actual)”. This variable shows the difference between the LFA model’s prediction and the actual data. Go to the Performance Profiler panel on left and change Sort By from Error R ...
Crime vs. demographic factors revisited: Application of data mining
... solving QP problems was introduced in Platt (1998). Thirdly, there is Least-Squares SVM (Suykens & Vandewalle, 1999) which is a reformulation of Vapnik’s SVM. Since SVMs were mentioned only for binary classification problem, various multi-class extensions (cases where the number of classes is greate ...
... solving QP problems was introduced in Platt (1998). Thirdly, there is Least-Squares SVM (Suykens & Vandewalle, 1999) which is a reformulation of Vapnik’s SVM. Since SVMs were mentioned only for binary classification problem, various multi-class extensions (cases where the number of classes is greate ...
Mining Frequent Patterns without Candidate Generation
... What does Apriori-like algorithm suffer from? – In situations with many frequent patterns, long patterns, or quite low minimum support threshold, it is costly. – It is tedious to repeatedly scan the database and check a large set of candidates by patterns matching. ...
... What does Apriori-like algorithm suffer from? – In situations with many frequent patterns, long patterns, or quite low minimum support threshold, it is costly. – It is tedious to repeatedly scan the database and check a large set of candidates by patterns matching. ...
Version2 - School of Computer Science
... inserted in a semi-random manner: the data are generated randomly with the same type and within the same range of original data (as shown in Appendix 3A and 3B). The inserted data can be considered as a kind of perturbed data. Unlike the initial objective of using the perturbed data to protect confi ...
... inserted in a semi-random manner: the data are generated randomly with the same type and within the same range of original data (as shown in Appendix 3A and 3B). The inserted data can be considered as a kind of perturbed data. Unlike the initial objective of using the perturbed data to protect confi ...
Chapter 8 - Data Miners Inc
... MBR Example – Rents in Tuxedo, NY • Classify nearest neighbors based on descriptive variables – population & median home prices (not geography in this example) • Range midpoint in 2 neighbors is $1,000 & $1,250 so Tuxedo rent should be $1,125; 2nd method yields rent of $977 • Actual midpoint rent i ...
... MBR Example – Rents in Tuxedo, NY • Classify nearest neighbors based on descriptive variables – population & median home prices (not geography in this example) • Range midpoint in 2 neighbors is $1,000 & $1,250 so Tuxedo rent should be $1,125; 2nd method yields rent of $977 • Actual midpoint rent i ...
Data Clustering Method for Very Large Databases using entropy
... becomes a poor fit as more points are clustered. In order to reduce this effect, we enhanced the heuristic by reprocessing a fraction of the points in the batch. After a batch of points is clustered, we select a fraction m of points in the batch that can be considered the worst fit for the clusters ...
... becomes a poor fit as more points are clustered. In order to reduce this effect, we enhanced the heuristic by reprocessing a fraction of the points in the batch. After a batch of points is clustered, we select a fraction m of points in the batch that can be considered the worst fit for the clusters ...
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.