View PDF - Department of Computer Science, CUSAT
... These techniques can be used to solve many of the real time challenges in the field of remote sensing, medical imaging, and scientific visualization and so on. Digital Image Processing also takes tremendous advances in technology such as mining, neural network etc., and combines them with the powerf ...
... These techniques can be used to solve many of the real time challenges in the field of remote sensing, medical imaging, and scientific visualization and so on. Digital Image Processing also takes tremendous advances in technology such as mining, neural network etc., and combines them with the powerf ...
Text Documents Clustering
... K-means algorithm with cosine similarity have reached almost the same values of F1 and incorrect clustered documents but despite small difference between F1 values and evaluating the time taken for clustering (Table V), K-means with cosine similarity results can be considered well because of conside ...
... K-means algorithm with cosine similarity have reached almost the same values of F1 and incorrect clustered documents but despite small difference between F1 values and evaluating the time taken for clustering (Table V), K-means with cosine similarity results can be considered well because of conside ...
- International Journal of Multidisciplinary Research and
... class and the contrasting class(es), Generalize both classes to the same high level concepts, Compare tuples with the same high level descriptions, Present for every tuple its description and two measures 1. Support - distribution within single class 2. Comparison - distribution between classes. Hig ...
... class and the contrasting class(es), Generalize both classes to the same high level concepts, Compare tuples with the same high level descriptions, Present for every tuple its description and two measures 1. Support - distribution within single class 2. Comparison - distribution between classes. Hig ...
A C - NDSU Computer Science
... reduce the number of itemsets that need to be counted (called candidate frequent itemsets C) Works on a level-by-level basis (i.e. uses frequent itemsets L from the previous to generate frequent itemsets at this level) ...
... reduce the number of itemsets that need to be counted (called candidate frequent itemsets C) Works on a level-by-level basis (i.e. uses frequent itemsets L from the previous to generate frequent itemsets at this level) ...
Statistical Themes and Lessons for Data Mining
... predict properties of a new sample, where it is assumed that the two samples are obtained from the same probability distribution. As with estimation, in prediction we are interested both in reliability and in uncertainty, often measured by the variance of the predictor. Prediction methods for this s ...
... predict properties of a new sample, where it is assumed that the two samples are obtained from the same probability distribution. As with estimation, in prediction we are interested both in reliability and in uncertainty, often measured by the variance of the predictor. Prediction methods for this s ...
CS2270412
... only those documents that contain two or more words that are separated by a specified number of words; a search for "Wikipedia" WITHIN2 "free" would retrieve only those documents in which the words "Wikipedia" and "free" occur within two words of each other. Regular expression: A regular expression ...
... only those documents that contain two or more words that are separated by a specified number of words; a search for "Wikipedia" WITHIN2 "free" would retrieve only those documents in which the words "Wikipedia" and "free" occur within two words of each other. Regular expression: A regular expression ...
Open-Source Tools for Data Mining - e
... loads the data (Dataset), shows a scatterplot (Scatterplot 1), selects a set of features (Define status 1), computes linear correlations (Linear correlation 1), selects a subset of instances based on a set of conditions (Rule-based selection 1), computes the correlation and a scatterplot for these in ...
... loads the data (Dataset), shows a scatterplot (Scatterplot 1), selects a set of features (Define status 1), computes linear correlations (Linear correlation 1), selects a subset of instances based on a set of conditions (Rule-based selection 1), computes the correlation and a scatterplot for these in ...
Data mining in Cloud Computing
... report fraud, and tax compliance. Produces new attributes as linear combination of existing attributes. Applicable for text data, latent semantic analysis, data compression, data decomposition and projection, and pattern recognition. ...
... report fraud, and tax compliance. Produces new attributes as linear combination of existing attributes. Applicable for text data, latent semantic analysis, data compression, data decomposition and projection, and pattern recognition. ...
Lecture 3
... occurs when essentially identical data appears in multiple variables, e.g. “date_of_birth”, “age” if not actually identical, will still slow building of model if actually identical can cause significant numerical computation problems for some models - even causing crashes ...
... occurs when essentially identical data appears in multiple variables, e.g. “date_of_birth”, “age” if not actually identical, will still slow building of model if actually identical can cause significant numerical computation problems for some models - even causing crashes ...
Basic Clustering Concepts & Algorithms
... For each document, reallocate the document to the cluster to which it has the highest similarity (shown in red in the above table). After the reallocation we have the following new clusters. Note that the previously unassigned D7 and D8 have been assigned, and that D1 and D6 have been reallocated fr ...
... For each document, reallocate the document to the cluster to which it has the highest similarity (shown in red in the above table). After the reallocation we have the following new clusters. Note that the previously unassigned D7 and D8 have been assigned, and that D1 and D6 have been reallocated fr ...
On Building Decision Trees from Large-scale Data in
... features after processing (for example, by taking dot products of bag-of-words representations). We do not consider this class of features in this paper, but naturally it would be useful to combine them (such as through a mixture model) for obtaining more accurate predictions. Sparse training instan ...
... features after processing (for example, by taking dot products of bag-of-words representations). We do not consider this class of features in this paper, but naturally it would be useful to combine them (such as through a mixture model) for obtaining more accurate predictions. Sparse training instan ...
Resolving Mobile Communique App Using Classification of Data
... upstart mobile apps, including Viber, Line and WeChat, etc. Mobile communication appsallow users to share ideas, pictures, posts, activities, events, and interests with people in their network.Nowadays, the Mobile apps used by all people (eg. WhatsApp,WeChat,MessageMe, Line, etc...). In this researc ...
... upstart mobile apps, including Viber, Line and WeChat, etc. Mobile communication appsallow users to share ideas, pictures, posts, activities, events, and interests with people in their network.Nowadays, the Mobile apps used by all people (eg. WhatsApp,WeChat,MessageMe, Line, etc...). In this researc ...
Ruiz`s Slides on Anomaly Detection.
... Anomaly score function: Given a data instance x from a dataset D, Alternate definitions: 1. f(x) = distance between x and its closest centroid 2. f(x) : (called relative distance) = ratio between the point's distance from the centroid to the median distance of all points in the cluster from the cent ...
... Anomaly score function: Given a data instance x from a dataset D, Alternate definitions: 1. f(x) = distance between x and its closest centroid 2. f(x) : (called relative distance) = ratio between the point's distance from the centroid to the median distance of all points in the cluster from the cent ...
a conceptual framework for predicting flood area in terengganu
... along with data mining algorithms will help to find correlations or patterns. Nowadays, we have infinite data but lacking in useful information. IBM stated that every day we create 2.5 quintillion bytes of data which 90% of the data in the world has been created in the last two years. So we need dat ...
... along with data mining algorithms will help to find correlations or patterns. Nowadays, we have infinite data but lacking in useful information. IBM stated that every day we create 2.5 quintillion bytes of data which 90% of the data in the world has been created in the last two years. So we need dat ...
Grid-based Support for Different Text Mining Tasks
... which consists of neurons characterized by n-dimensional weight vector (same dimension as input vectors of the objects). Specific feature of SOM-based algorithms is realization of topology preserving mapping. Neurons are ordered in some regular structure (e.g. usually it is simple two-dimensional Gr ...
... which consists of neurons characterized by n-dimensional weight vector (same dimension as input vectors of the objects). Specific feature of SOM-based algorithms is realization of topology preserving mapping. Neurons are ordered in some regular structure (e.g. usually it is simple two-dimensional Gr ...
PINKDD-workshop-keyn.. - The University of Texas at Dallas
... geospatial applications - Once the image of my house is on Google Earth, then how much privacy can I have? I may want my location to be private, but does it make sense if a camera can capture a picture of me? - If there are sensors all over the place, is it meaningful to have privacy preserving surv ...
... geospatial applications - Once the image of my house is on Google Earth, then how much privacy can I have? I may want my location to be private, but does it make sense if a camera can capture a picture of me? - If there are sensors all over the place, is it meaningful to have privacy preserving surv ...
Data Science with R Decision Trees with Rattle
... 5. Note also the variable RISK Adjustment is set to have a role of Risk (this is based on its name). For now, choose to give it the role of an Input variable. 6. Choose to Partition the data. In fact, leave the 70/15/15 percentage split in the Partition text box as it is. Also, ensure the Partition ...
... 5. Note also the variable RISK Adjustment is set to have a role of Risk (this is based on its name). For now, choose to give it the role of an Input variable. 6. Choose to Partition the data. In fact, leave the 70/15/15 percentage split in the Partition text box as it is. Also, ensure the Partition ...
density based subspace clustering
... algorithm. Each dimension will be tested to investigate whether having a relationship with the data on another cluster, using proposed subspace clustering algorithms. If the data have a relationship, it will be classified as a subspace. Any data on the subspace clusters will then be tested again wit ...
... algorithm. Each dimension will be tested to investigate whether having a relationship with the data on another cluster, using proposed subspace clustering algorithms. If the data have a relationship, it will be classified as a subspace. Any data on the subspace clusters will then be tested again wit ...
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.