A Literature Review on Kidney Disease Prediction using Data
... form of association rules or some other internal formalism. Support Vector Machine (SVM): Support vector machine (SVM) is an algorithm that attempts to find a linear separator (hyper-plane) between the data points of two classes in multidimensional space. SVMs are well suited to dealing with interac ...
... form of association rules or some other internal formalism. Support Vector Machine (SVM): Support vector machine (SVM) is an algorithm that attempts to find a linear separator (hyper-plane) between the data points of two classes in multidimensional space. SVMs are well suited to dealing with interac ...
Introduction to knowledge discovery in databases
... Having completed the above four steps, the following four steps are related to the Data Mining part, where the focus is on the algorithmic aspects employed for each project: 5. Choosing the appropriate Data Mining task. We are now ready to decide on which type of Data Mining to use, for example, cla ...
... Having completed the above four steps, the following four steps are related to the Data Mining part, where the focus is on the algorithmic aspects employed for each project: 5. Choosing the appropriate Data Mining task. We are now ready to decide on which type of Data Mining to use, for example, cla ...
Performance Analysis of Decision Tree Algorithms for Breast Cancer
... algorithms compared using WEKA tool environment and results are discussed10. This would classify the breast cancer dataset into the three breast cancer type (categories), depending on their characteristics, performance and other features. Several types of classification algorithms were selected and ...
... algorithms compared using WEKA tool environment and results are discussed10. This would classify the breast cancer dataset into the three breast cancer type (categories), depending on their characteristics, performance and other features. Several types of classification algorithms were selected and ...
Chapter 22: Advanced Querying and Information Retrieval
... statistical rules and patterns from large databases. A data warehouse archives information gathered from multiple sources, and stores it under a unified schema, at a single site. ...
... statistical rules and patterns from large databases. A data warehouse archives information gathered from multiple sources, and stores it under a unified schema, at a single site. ...
084
... (gene expression) of thousands of genes. It has 3 phases: Place thousands of different one-strand chunks of RNA in minuscule wells on the surface of a small glass chip Spread genetic material obtained by a cell experiment one wishes to perform Use a laser scanner and computer to measure the am ...
... (gene expression) of thousands of genes. It has 3 phases: Place thousands of different one-strand chunks of RNA in minuscule wells on the surface of a small glass chip Spread genetic material obtained by a cell experiment one wishes to perform Use a laser scanner and computer to measure the am ...
Distributed Computing Environment: Approaches and Applications
... deals with the problem of computing a “global” model from large and inherently distributed databases. The goal of meta-learning is to compute a number of independent model (classifiers) by applying learning programs in parallel, that is, without transferring or directly accessing the data sites. Sto ...
... deals with the problem of computing a “global” model from large and inherently distributed databases. The goal of meta-learning is to compute a number of independent model (classifiers) by applying learning programs in parallel, that is, without transferring or directly accessing the data sites. Sto ...
Online Spatial Data Analysis and Visualization System
... the properties near the big lake are cheaper, while the properties along the west are more expensive. ...
... the properties near the big lake are cheaper, while the properties along the west are more expensive. ...
An Hybrid Recommendation System
... • Taxonomy of recommender systems – Targeted customer inputs ...
... • Taxonomy of recommender systems – Targeted customer inputs ...
Using consumer behavior data to reduce energy
... algorithms, as well as genetic algorithms, from the family of heuristic algorithms, are suitable for finding frequent patterns in large datasets. In this work, we consider only deterministic algorithm, since they are able to find patterns in a reasonable amount of time and do not have the disadvanta ...
... algorithms, as well as genetic algorithms, from the family of heuristic algorithms, are suitable for finding frequent patterns in large datasets. In this work, we consider only deterministic algorithm, since they are able to find patterns in a reasonable amount of time and do not have the disadvanta ...
Visual Analysis and Knowledge Discovery for Text
... relationships at once, even in large amounts of data. Visualization is an effective enabler for exploratory analysis [52], making it a powerful tool for gaining insight into unexplored data sets. Visual Analytics is an interdisciplinary field based on information visualization, knowledge discovery a ...
... relationships at once, even in large amounts of data. Visualization is an effective enabler for exploratory analysis [52], making it a powerful tool for gaining insight into unexplored data sets. Visual Analytics is an interdisciplinary field based on information visualization, knowledge discovery a ...
Data Mining and Visualization of Twin
... the rst law of geography: everything is related to everything else but nearby things are more related than distant things. Knowledge discovery techniques which ignore spatial autocorrelation typically perform poorly in the presence of spatial data. Spatial statistics techniques on the other hand do ...
... the rst law of geography: everything is related to everything else but nearby things are more related than distant things. Knowledge discovery techniques which ignore spatial autocorrelation typically perform poorly in the presence of spatial data. Spatial statistics techniques on the other hand do ...
Spatial Data Mining by Satoru Hozumi
... Process of discovering groups in large databases Spatial view: rows in a database = points in a multidimentional space. Visualization may reveal interesting groups ...
... Process of discovering groups in large databases Spatial view: rows in a database = points in a multidimentional space. Visualization may reveal interesting groups ...
Journal of Information Science
... LPS-PFP algorithms. In our experiment, the proposed approach is compared with other existing sequence-pattern-mining algorithms (MRApriori and PFP). We concluded that our proposed approach for web-usage mining outperforms the others. ...
... LPS-PFP algorithms. In our experiment, the proposed approach is compared with other existing sequence-pattern-mining algorithms (MRApriori and PFP). We concluded that our proposed approach for web-usage mining outperforms the others. ...
data mining for a web-based educational system
... propose an algorithm for the discovery of “interesting” association rules within a webbased educational system. The main focus is on mining interesting contrast rules, which are sets of conjunctive rules describing interesting characteristics of different segments within a population. In the context ...
... propose an algorithm for the discovery of “interesting” association rules within a webbased educational system. The main focus is on mining interesting contrast rules, which are sets of conjunctive rules describing interesting characteristics of different segments within a population. In the context ...
Hierarchical Volume Visualization
... knowledge into multiple simultaneous presentations. This method allows a user to easily discover knowledge relationships and exceptions. The third idea is to define new visual interfaces to plug into existing graphic toolkits, such as TGS' 3DMSJava and Inxight's Hyperbolic Tree Toolkits, thus expand ...
... knowledge into multiple simultaneous presentations. This method allows a user to easily discover knowledge relationships and exceptions. The third idea is to define new visual interfaces to plug into existing graphic toolkits, such as TGS' 3DMSJava and Inxight's Hyperbolic Tree Toolkits, thus expand ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... Data mining is the way of extracting useful information and discovering knowledge patterns that may be used for decision making [7]. Several data mining techniques are association rule, clustering, classification and prediction, neural networks, decision tree, etc. Application of data mining techniq ...
... Data mining is the way of extracting useful information and discovering knowledge patterns that may be used for decision making [7]. Several data mining techniques are association rule, clustering, classification and prediction, neural networks, decision tree, etc. Application of data mining techniq ...
Cluster analysis
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It will often be necessary to modify data preprocessing and model parameters until the result achieves the desired properties.Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek βότρυς ""grape"") and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals.Cluster analysis was originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Zubin in 1938 and Robert Tryon in 1939 and famously used by Cattell beginning in 1943 for trait theory classification in personality psychology.