Modeling Human Location Data with Mixtures of Kernel
... propose a hierarchical extension of the traditional kernel approach that can avoid the over-fitting problem by systematically smoothing an individual’s data towards the population data. We demonstrate how adaptive bandwidth techniques provide better quality density estimates compared to fixed bandwi ...
... propose a hierarchical extension of the traditional kernel approach that can avoid the over-fitting problem by systematically smoothing an individual’s data towards the population data. We demonstrate how adaptive bandwidth techniques provide better quality density estimates compared to fixed bandwi ...
Computational Movement Analysis
... 3.3 Segmenting Trajectories and Trajectory Simplification An important piece of information in many application fields is the activity the object is performing during the time it is tracked. Can, for example, in a transportation context the mode of transport be inferred from the trajectory? And in b ...
... 3.3 Segmenting Trajectories and Trajectory Simplification An important piece of information in many application fields is the activity the object is performing during the time it is tracked. Can, for example, in a transportation context the mode of transport be inferred from the trajectory? And in b ...
lecture19b_pattern_discovery
... • Addresses “bump-hunting” problem: – Assume we have a target variable Y • Y could be real-valued or a binary class variable ...
... • Addresses “bump-hunting” problem: – Assume we have a target variable Y • Y could be real-valued or a binary class variable ...
Combining Usage, Content, and Structure Data to Improve Web Site
... represented by the clusters of pages in the missions, are used by the recommendation engine, we need to provide an opportunity for these rarely visited or newly added pages to be included in the clusters. Otherwise, they would never be recommended. To alleviate this problem, we expand our clusters t ...
... represented by the clusters of pages in the missions, are used by the recommendation engine, we need to provide an opportunity for these rarely visited or newly added pages to be included in the clusters. Otherwise, they would never be recommended. To alleviate this problem, we expand our clusters t ...
ESDA jul2016 Session Presentation
... framework of a single model. In other words, in a multivariate analysis with multiple predictors and a single responce variable, if the conditional distrubute of the one predictor variable in relation to the responce variable is linear then a linear function will be applied, while if the conditional ...
... framework of a single model. In other words, in a multivariate analysis with multiple predictors and a single responce variable, if the conditional distrubute of the one predictor variable in relation to the responce variable is linear then a linear function will be applied, while if the conditional ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... visiting a webpage, (ii) extract the information we need, and (iii) repeat these steps. Using this idea we identified three conceptually different type of crawling method each of which is suitable to collect data for different type of analysis and related problem statement. The first and, we might s ...
... visiting a webpage, (ii) extract the information we need, and (iii) repeat these steps. Using this idea we identified three conceptually different type of crawling method each of which is suitable to collect data for different type of analysis and related problem statement. The first and, we might s ...
Decision Tree-Based Data Characterization for Meta
... and information theory based measures. Statistical characteristics are mainly appropriate for continuous attributes, while information theory based measures are more appropriate for discrete attributes. Linder and Studer [13] provide an extensive list of information and statistical measures of a dat ...
... and information theory based measures. Statistical characteristics are mainly appropriate for continuous attributes, while information theory based measures are more appropriate for discrete attributes. Linder and Studer [13] provide an extensive list of information and statistical measures of a dat ...
Data Science and Digital Art History
... is quite recent, it is quite useful as it acts as an umbrella for currently most frequently used methods of computational data analysis. (Alternatively, I could have chosen machine learning or data mining as the key term for this article, but since data science includes their methods, I decided that ...
... is quite recent, it is quite useful as it acts as an umbrella for currently most frequently used methods of computational data analysis. (Alternatively, I could have chosen machine learning or data mining as the key term for this article, but since data science includes their methods, I decided that ...
Printable version - University of Alberta
... Data regression consists of fitting the data to a function. A linear regression for instance, finds the line to fit 2 variables so that one variable can predict the other. More variables can be involved in a multiple linear regression. ...
... Data regression consists of fitting the data to a function. A linear regression for instance, finds the line to fit 2 variables so that one variable can predict the other. More variables can be involved in a multiple linear regression. ...
OCARA AS METHOD OF CLASSIFICATION AND ASSOCIATION
... - Repeat process for each branch until all cases in the branch have the same class. Choosing which attribute to be a root is based on highest gain of each attribute ...
... - Repeat process for each branch until all cases in the branch have the same class. Choosing which attribute to be a root is based on highest gain of each attribute ...
Data Analysis and Mining
... E.g., suppose table itemcategory(item-name, category) gives the category of each item. Then select category, item-name, sum(number) from sales, itemcategory ...
... E.g., suppose table itemcategory(item-name, category) gives the category of each item. Then select category, item-name, sum(number) from sales, itemcategory ...
Survey of existing data mining techniques, methods and guidelines
... what is going on inside the data at the present time and therefore augment the understanding of the people, products, or processes that generated the data (see Figure 2). Predictive data mining methods in common use are classification, regression, time series analysis, and prediction: 1. Classificat ...
... what is going on inside the data at the present time and therefore augment the understanding of the people, products, or processes that generated the data (see Figure 2). Predictive data mining methods in common use are classification, regression, time series analysis, and prediction: 1. Classificat ...
Visual Reconciliation of Alternative Similarity Spaces
... paradigm: visual reconciliation, which is an iterative, human-in-theloop refinement strategy for reconciling alternative similarity spaces. The reconciliation technique involves synergy among computational methods, adaptive visual representations, and a flexible interaction model, for communicating ...
... paradigm: visual reconciliation, which is an iterative, human-in-theloop refinement strategy for reconciling alternative similarity spaces. The reconciliation technique involves synergy among computational methods, adaptive visual representations, and a flexible interaction model, for communicating ...
Steven F. Ashby Center for Applied Scientific
... Traditional techniques infeasible for raw data Data mining may help scientists – in classifying and segmenting data – in Hypothesis Formation ...
... Traditional techniques infeasible for raw data Data mining may help scientists – in classifying and segmenting data – in Hypothesis Formation ...
Research on Personalized Recommendation Based on Web Usage
... users must be measured. Some measure algorithms often are applied, such as cosine similarity, correlation similarity and adjusted cosine similarity, to measure users’ similarity degree. The precise computation of goal user's neighbours is the successful key of CF model. It is necessary to attempt ne ...
... users must be measured. Some measure algorithms often are applied, such as cosine similarity, correlation similarity and adjusted cosine similarity, to measure users’ similarity degree. The precise computation of goal user's neighbours is the successful key of CF model. It is necessary to attempt ne ...
Current Data Mining Trends, Techniques, Applications and Issues
... classifier training algorithm uses these pre-classified examples to determine the set of parameters required for proper discrimination. The algorithm then encodes these parameters into a model called a classifier. C. Regression Analysis Regression Analysis is one of the techniques of multivariable a ...
... classifier training algorithm uses these pre-classified examples to determine the set of parameters required for proper discrimination. The algorithm then encodes these parameters into a model called a classifier. C. Regression Analysis Regression Analysis is one of the techniques of multivariable a ...
A Review Approach on various form of Apriori with
... Extensive growth of data gives the motivation to find meaningful patterns among the huge data. Sequential pattern provides us interesting relationships between different items in sequential database. Association Rules Mining (ARM) is a function of DM research domain and arise many researchers intere ...
... Extensive growth of data gives the motivation to find meaningful patterns among the huge data. Sequential pattern provides us interesting relationships between different items in sequential database. Association Rules Mining (ARM) is a function of DM research domain and arise many researchers intere ...
Studies on Computational Learning via
... throughout the thesis is amalgamation of two processes; discretization of continuous objects and learning from such objects provided by data. Machine learning, or data mining and knowledge discovery, has been rapidly developed in recent years and is now becoming a huge topic in not only research com ...
... throughout the thesis is amalgamation of two processes; discretization of continuous objects and learning from such objects provided by data. Machine learning, or data mining and knowledge discovery, has been rapidly developed in recent years and is now becoming a huge topic in not only research com ...
CS 401R, section 1: Statistical Natural Language Processing
... Iterative procedure: 1. Guess some initial parameters for the model 2. Use model to estimate partial label counts for all docs (E-step) 3. Use the new complete data to learn better model (M-step) 4. Repeat steps 2 and 3 until convergence ...
... Iterative procedure: 1. Guess some initial parameters for the model 2. Use model to estimate partial label counts for all docs (E-step) 3. Use the new complete data to learn better model (M-step) 4. Repeat steps 2 and 3 until convergence ...
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.