A Decision Tree Algorithm Based System for Predicting Crime in the
... eradicate crime. The Directorate of Students and Services Development (DSSD) are responsible for investigating and detecting criminals of any crime committed within the Redeemer’s University. DSSD faces major challenges when it comes to detecting the real perpetrators of several crimes. An improveme ...
... eradicate crime. The Directorate of Students and Services Development (DSSD) are responsible for investigating and detecting criminals of any crime committed within the Redeemer’s University. DSSD faces major challenges when it comes to detecting the real perpetrators of several crimes. An improveme ...
Data mining(DM)
... For each tuple tr from R For each tuple ts from S if join attribute of tr equals to join attribute of ts form output tuple by concatenating tr and ts Advanced Technology for Knowledge Management ...
... For each tuple tr from R For each tuple ts from S if join attribute of tr equals to join attribute of ts form output tuple by concatenating tr and ts Advanced Technology for Knowledge Management ...
Quadratic Programming Feature Selection
... method is named Quadratic Programming Feature Selection (QPFS) because it is based on efficient quadratic programming (Bertsekas, 1999). We introduce an objective function with quadratic and linear terms. The quadratic term captures the dependence (that is, similarity, correlation, or mutual informa ...
... method is named Quadratic Programming Feature Selection (QPFS) because it is based on efficient quadratic programming (Bertsekas, 1999). We introduce an objective function with quadratic and linear terms. The quadratic term captures the dependence (that is, similarity, correlation, or mutual informa ...
Top 10 algorithms in data mining Algorithms
... or by using a different distance measure that is more appropriate for the dataset. For example, information-theoretic clustering uses the KL-divergence to measure the distance between two data points representing two discrete probability distributions. It has been recently shown that if one measures ...
... or by using a different distance measure that is more appropriate for the dataset. For example, information-theoretic clustering uses the KL-divergence to measure the distance between two data points representing two discrete probability distributions. It has been recently shown that if one measures ...
Scaling Clustering Algorithms to Large Databases
... sampled singleton data points assigned to cluster j. All data items within that radius are sent to the discard set DSj. The sufficient statistics for data points discarded by this method are merged with the DSj of points previously compressed in this phase on past data samples. The second primary co ...
... sampled singleton data points assigned to cluster j. All data items within that radius are sent to the discard set DSj. The sufficient statistics for data points discarded by this method are merged with the DSj of points previously compressed in this phase on past data samples. The second primary co ...
Data Mining and Data Pre-processing for Big Data
... Abstract— Big Data is a term which is used to describe massive amount of data generating from digital sources or the internet usually characterized by 3 V’s i.e. Volume, Velocity and Variety. From the past few years data is exponentially growing due to the use of connected devices such as smart phon ...
... Abstract— Big Data is a term which is used to describe massive amount of data generating from digital sources or the internet usually characterized by 3 V’s i.e. Volume, Velocity and Variety. From the past few years data is exponentially growing due to the use of connected devices such as smart phon ...
Scaling Clustering Algorithms to Large Databases
... sampled singleton data points assigned to cluster j. All data items within that radius are sent to the discard set DSj. The sufficient statistics for data points discarded by this method are merged with the DSj of points previously compressed in this phase on past data samples. The second primary co ...
... sampled singleton data points assigned to cluster j. All data items within that radius are sent to the discard set DSj. The sufficient statistics for data points discarded by this method are merged with the DSj of points previously compressed in this phase on past data samples. The second primary co ...
Detecting Suspicious Claims
... •Big hurdle in initially building a data set for analysis • Company skill set, hardware, and dedicated resources • Some important factors were not historically collected •Text Mining as an information extraction tool is quite valuable •Fielding sophisticated models can depend significantly on IT •Co ...
... •Big hurdle in initially building a data set for analysis • Company skill set, hardware, and dedicated resources • Some important factors were not historically collected •Text Mining as an information extraction tool is quite valuable •Fielding sophisticated models can depend significantly on IT •Co ...
An Indian Journal - Trade Science Inc
... (3)It needs better prior knowledge for decision of input parameters. Require the user to enter specific parameters, such as the hard k-means algorithm and fuzzy k-means algorithm are required to enter the desired number of clusters k clusters before most clustering algorithms during operation. Moreo ...
... (3)It needs better prior knowledge for decision of input parameters. Require the user to enter specific parameters, such as the hard k-means algorithm and fuzzy k-means algorithm are required to enter the desired number of clusters k clusters before most clustering algorithms during operation. Moreo ...
Fast mining of frequent tree structures by hashing and indexing
... includes the algorithm of Wang et al. [7,29]. This method is based on the original Apriori association rule mining paradigm and implements a generate-and-test strategy. The second category includes the works that devised an incremental algorithm that simultaneously constructs the set of frequent pat ...
... includes the algorithm of Wang et al. [7,29]. This method is based on the original Apriori association rule mining paradigm and implements a generate-and-test strategy. The second category includes the works that devised an incremental algorithm that simultaneously constructs the set of frequent pat ...
A Survey on Nearest Neighbor Search Methods
... S is calculated and each point that has the lowest distance is chosen as a result. The main problem in this technique is unsalable that in high dimensional or by increasing the points in space, the speed of searching is really decreased. kNN technique for the first time in [42] has been presented fo ...
... S is calculated and each point that has the lowest distance is chosen as a result. The main problem in this technique is unsalable that in high dimensional or by increasing the points in space, the speed of searching is really decreased. kNN technique for the first time in [42] has been presented fo ...
SQL Server Analysis Services Data Mining Overview
... Data Mining addresses a wide variety of problems SQL Server 2005 contains a fullfeatured set of data mining tools and API’s for the creation and deployment of data mining solutions. ...
... Data Mining addresses a wide variety of problems SQL Server 2005 contains a fullfeatured set of data mining tools and API’s for the creation and deployment of data mining solutions. ...
Multi-Document Content Summary Generated via Data Merging Scheme
... Single/Complete/Average Link, and these cluster ensemble algorithm called as CSPA. These algorithms were run with the different combinations of their different parameters, resulting in different algorithmic instantiations. Thus, it is contribution of our work, to compare their relative performances ...
... Single/Complete/Average Link, and these cluster ensemble algorithm called as CSPA. These algorithms were run with the different combinations of their different parameters, resulting in different algorithmic instantiations. Thus, it is contribution of our work, to compare their relative performances ...
Data Mining
... Statistical methods (including both hierarchical and nonhierarchical), such as k-means, k-modes, and so on Neural networks (adaptive resonance theory [ART], self-organizing map [SOM]) Fuzzy logic (e.g., fuzzy c-means algorithm) Genetic algorithms ...
... Statistical methods (including both hierarchical and nonhierarchical), such as k-means, k-modes, and so on Neural networks (adaptive resonance theory [ART], self-organizing map [SOM]) Fuzzy logic (e.g., fuzzy c-means algorithm) Genetic algorithms ...
Prediction of Probability of Chronic Diseases and Providing Relative
... prominent causes for deaths worldwide. Fatality rates owing to chronic diseases are accelerating globally, growing across every region, encompassing all socioeconomic classes and thus contributing to financial burden. According to the World Health Report, by 2020 their contribution is estimated to r ...
... prominent causes for deaths worldwide. Fatality rates owing to chronic diseases are accelerating globally, growing across every region, encompassing all socioeconomic classes and thus contributing to financial burden. According to the World Health Report, by 2020 their contribution is estimated to r ...
Solutions
... The major task of on-line operational database systems is to perform on-line transaction and query Processing. These systems are called on-line transaction processing (OLTP) systems. They cover Most of the day-to-day operations of an organization, such as purchasing, inventory, manufacturing, bankin ...
... The major task of on-line operational database systems is to perform on-line transaction and query Processing. These systems are called on-line transaction processing (OLTP) systems. They cover Most of the day-to-day operations of an organization, such as purchasing, inventory, manufacturing, bankin ...
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.