Stock Trend Prediction by Using K-Means and
... The framework of the proposed system is shown in Fig. 1. Modularized design can help users apply the proposed method to different type algorithm easily. The system includes these modules: Chart Extractor, Chart Recognition Analyzer, Chart Clustering Constructor, Sequential Chart Pattern Finder, Stoc ...
... The framework of the proposed system is shown in Fig. 1. Modularized design can help users apply the proposed method to different type algorithm easily. The system includes these modules: Chart Extractor, Chart Recognition Analyzer, Chart Clustering Constructor, Sequential Chart Pattern Finder, Stoc ...
Knowledge Discovery and Data Mining
... Work submitted by a student that is the work of another student or any other person is considered plagiarism. Read Sections 26.1.4 and 26.1.5 of the University of Alberta calendar. Cases of plagiarism are immediately referred to the Dean of Science, who determines what course of action is appropriat ...
... Work submitted by a student that is the work of another student or any other person is considered plagiarism. Read Sections 26.1.4 and 26.1.5 of the University of Alberta calendar. Cases of plagiarism are immediately referred to the Dean of Science, who determines what course of action is appropriat ...
a survey of data mining and knowledge discovery software
... The underlying data model: Many tools that are available today just take their input in form of one table, where each sample case (record) has a fixed number of attributes. Other tools are based on the relational model and allow querying of the underlying database. Object-oriented and nonstandard da ...
... The underlying data model: Many tools that are available today just take their input in form of one table, where each sample case (record) has a fixed number of attributes. Other tools are based on the relational model and allow querying of the underlying database. Object-oriented and nonstandard da ...
A Requirements Analysis for Parallel KDD Systems
... structures for PKDD is required (Gaede and Gunther 1998). The vast amount of work on parallel relational query operators, particularly parallel join algorithms, is also of relevance (Pirahesh et al. 1990). The use of DBMS views (Oszu and Valduriez 1999) to restrict the access of a DBMS user to a su ...
... structures for PKDD is required (Gaede and Gunther 1998). The vast amount of work on parallel relational query operators, particularly parallel join algorithms, is also of relevance (Pirahesh et al. 1990). The use of DBMS views (Oszu and Valduriez 1999) to restrict the access of a DBMS user to a su ...
Week 8-ppt - Monash University
... • Machine learning can be considered as a search problem. We wish to find the correct hypothesis from among many. –If there are only a few hypotheses we could try them all but if there are an infinite number we need a better strategy. –If we have a measure of the quality of the hypothesis we can use ...
... • Machine learning can be considered as a search problem. We wish to find the correct hypothesis from among many. –If there are only a few hypotheses we could try them all but if there are an infinite number we need a better strategy. –If we have a measure of the quality of the hypothesis we can use ...
Open Challenges for Data Stream Mining Research
... This article builds upon discussions at the International Workshop on Real-World Challenges for Data Stream Mining (RealStream)1 in September 2013, in Prague, Czech Republic. Several related position papers are available. Dietterich [10] presents a discussion focused on predictive modeling technique ...
... This article builds upon discussions at the International Workshop on Real-World Challenges for Data Stream Mining (RealStream)1 in September 2013, in Prague, Czech Republic. Several related position papers are available. Dietterich [10] presents a discussion focused on predictive modeling technique ...
Outlier Detection for Business Intelligence using Data
... information from large datasets, using it for organizational decision making [1]. Whereas an outlier is a data object that deviates significantly from the normal objects as if it were generated by a different mechanism. The identification ofoutliers can lead to the discovery of useful and meaningful ...
... information from large datasets, using it for organizational decision making [1]. Whereas an outlier is a data object that deviates significantly from the normal objects as if it were generated by a different mechanism. The identification ofoutliers can lead to the discovery of useful and meaningful ...
Microsoft PowerPoint - NCRM EPrints Repository
... 2D scatterplots? Structure is unclear (13 x 12) / 2 = 78 plots needed Principal components analysis? 2 PCs explain 49% of the variance 3 PCs explain 65% of the variance Should be > 85% for confident representation Fisher’s iris dataset (4 variables) is 95% ...
... 2D scatterplots? Structure is unclear (13 x 12) / 2 = 78 plots needed Principal components analysis? 2 PCs explain 49% of the variance 3 PCs explain 65% of the variance Should be > 85% for confident representation Fisher’s iris dataset (4 variables) is 95% ...
BAYDA: Software for Bayesian Classification and Feature Selection
... in the sense that they could be easily “boosted up” by several percentage units by using k-fold crossvalidation with a single, conveniently chosen data partitioning. For this reason, we argue that our 0/1-score classification results, being better than those reported in, e.g., (Kohavi & John 1997; F ...
... in the sense that they could be easily “boosted up” by several percentage units by using k-fold crossvalidation with a single, conveniently chosen data partitioning. For this reason, we argue that our 0/1-score classification results, being better than those reported in, e.g., (Kohavi & John 1997; F ...
Visual Exploratory Data Analysis of Traffic Volume
... Considered that the detectors are spatially contiguous, the classical K-means partitioning clustering algorithm is used to identify the clusters in Fig. 6, it aims to divide the data set into several homogeneous clusters, which may not overlap with each ...
... Considered that the detectors are spatially contiguous, the classical K-means partitioning clustering algorithm is used to identify the clusters in Fig. 6, it aims to divide the data set into several homogeneous clusters, which may not overlap with each ...
Web Data Mining: Exploring Hidden Patterns, its Types and Web
... link the information of its own website to navigate and cluster information into site maps. The hyperlinks present within a website provide useful information regarding the connection between different documents. The web can be considered as a directed graph whose nodes are the documents and the edg ...
... link the information of its own website to navigate and cluster information into site maps. The hyperlinks present within a website provide useful information regarding the connection between different documents. The web can be considered as a directed graph whose nodes are the documents and the edg ...
Introducing A Hybrid Data Mining Model to Evaluate Customer Loyalty
... • K-Nearest Neighbors: In this method, there is only one adjustable parameter: K. This value was changed in the range from 5 to 15. The best parameter combination was obtained here with the value 13 for the parameter K. In Bagging and Boosting models of this method, the number of Bag parameter for B ...
... • K-Nearest Neighbors: In this method, there is only one adjustable parameter: K. This value was changed in the range from 5 to 15. The best parameter combination was obtained here with the value 13 for the parameter K. In Bagging and Boosting models of this method, the number of Bag parameter for B ...
Mining periodic patterns in time-series databases - CEUR
... formulated as “All nonempty subsets of a frequent itemset must also be frequent”, i.e. an itemset is frequent only if all of its sub-itemsets are frequent. This observation applies to construct k+1-patterns based on the found k-patterns set. But 1-patterns are generally detected by a simple search a ...
... formulated as “All nonempty subsets of a frequent itemset must also be frequent”, i.e. an itemset is frequent only if all of its sub-itemsets are frequent. This observation applies to construct k+1-patterns based on the found k-patterns set. But 1-patterns are generally detected by a simple search a ...
Classification of Breast Cancer Cells Using JMP
... texture, smoothness, compactness, number of concave regions and size of concavities (a concavity is an indentation in the cell nucleus), symmetry, and fractal dimension of the boundary (a higher value means a less regular contour). (For more detail on these characteristics, see Street, WN, et al, 19 ...
... texture, smoothness, compactness, number of concave regions and size of concavities (a concavity is an indentation in the cell nucleus), symmetry, and fractal dimension of the boundary (a higher value means a less regular contour). (For more detail on these characteristics, see Street, WN, et al, 19 ...
Why Anomaly Detection?
... – If attributes are independent, we expect region to contain a fraction fk of the records – If there are N points, we can measure sparsity of a cube D as: ...
... – If attributes are independent, we expect region to contain a fraction fk of the records – If there are N points, we can measure sparsity of a cube D as: ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... a nonparametric, unsupervised approach to the analysis of data. Dan Li et al. [7] adopted the idea of nearestneighbor rule, a novel fuzzy c-means algorithm for incomplete data based on nearest-neighbor intervals is proposed. Medical profiles are subjected to the uncertainty of missing attributes, th ...
... a nonparametric, unsupervised approach to the analysis of data. Dan Li et al. [7] adopted the idea of nearestneighbor rule, a novel fuzzy c-means algorithm for incomplete data based on nearest-neighbor intervals is proposed. Medical profiles are subjected to the uncertainty of missing attributes, th ...
The Nonlinear Statistics of High-Contrast Patches in Natural Images
... space. The complexity of the data can partially be seen in the Haar wavelet statistics of natural images (Huang and Mumford, 1999; Huang et al., 2000). Take, for example, the 3D joint distribution of horizontal, vertical, and diagonal wavelet coefficients of natural images. Figure 1 shows that the e ...
... space. The complexity of the data can partially be seen in the Haar wavelet statistics of natural images (Huang and Mumford, 1999; Huang et al., 2000). Take, for example, the 3D joint distribution of horizontal, vertical, and diagonal wavelet coefficients of natural images. Figure 1 shows that the e ...
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.