View/Download-PDF - International Journal of Computer Science
... Let H be some hypothesis that the data tuple X belongs to a specified class C, X be a data tuple. P(H/X) - is the posterior probability of H conditioned on X. P(H) - is the prior probability of H. P(X/H) - is the posterior probability of X conditioned on H. P(X) - is prior probability of X. P (H/X) ...
... Let H be some hypothesis that the data tuple X belongs to a specified class C, X be a data tuple. P(H/X) - is the posterior probability of H conditioned on X. P(H) - is the prior probability of H. P(X/H) - is the posterior probability of X conditioned on H. P(X) - is prior probability of X. P (H/X) ...
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 ...
Client`s Logo/Name
... – Product preferences,Income,household size and hobbies All customer tombstone information as well as purchase information related to products bought has been summarized and stored onto a data mart. As a marketer and analyst, how would you use the information to develop a ...
... – Product preferences,Income,household size and hobbies All customer tombstone information as well as purchase information related to products bought has been summarized and stored onto a data mart. As a marketer and analyst, how would you use the information to develop a ...
RDF2Vec: RDF Graph Embeddings for Data Mining
... where each vector index represents one word. While such approaches are simple and robust, they suffer from several drawbacks, e.g., high dimensionality and severe data sparsity, which limits the performances of such techniques. To overcome such limitations, neural language models have been proposed, ...
... where each vector index represents one word. While such approaches are simple and robust, they suffer from several drawbacks, e.g., high dimensionality and severe data sparsity, which limits the performances of such techniques. To overcome such limitations, neural language models have been proposed, ...
PDF - JMLR Workshop and Conference Proceedings
... stream. We are interested in • classifying tweets in real time • detecting changes • showing what are the changes in the most used terms Our main goal is to build a system able to train and test from the Twitter streaming API continuously. The input items are the tweets obtained from the Twitter str ...
... stream. We are interested in • classifying tweets in real time • detecting changes • showing what are the changes in the most used terms Our main goal is to build a system able to train and test from the Twitter streaming API continuously. The input items are the tweets obtained from the Twitter str ...
Complete Paper
... Learning Vector Quantization (LVQ) is a local classification algorithm, where classification boundaries are locally approximated, the difference being that instead of using all training dataset points, LVQ uses only a prototype vectors set. This ensures efficient classification as vectors number nee ...
... Learning Vector Quantization (LVQ) is a local classification algorithm, where classification boundaries are locally approximated, the difference being that instead of using all training dataset points, LVQ uses only a prototype vectors set. This ensures efficient classification as vectors number nee ...
Schematic Discrepancy - University at Buffalo
... • For years researchers have developed many tools to visualize association rules. • However, few of these tools can handle more than dozens of rules, and none of them can effectively manage rules with multiple antece-dents. ...
... • For years researchers have developed many tools to visualize association rules. • However, few of these tools can handle more than dozens of rules, and none of them can effectively manage rules with multiple antece-dents. ...
Overview of Data Warehouse and Data Mining
... predict whether a newly arrived customer will spend more than 100$ at a department store. Data-mining techniques: The following list describes many datamining techniques in use today. Each of these techniques exists in several variations and can be applied to one or more of the categories above. ...
... predict whether a newly arrived customer will spend more than 100$ at a department store. Data-mining techniques: The following list describes many datamining techniques in use today. Each of these techniques exists in several variations and can be applied to one or more of the categories above. ...
Comparative Study of Quality Measures of Sequential Rules for the
... optimum), which can in some cases be far from optimal. A naive solution to this problem is to run these algorithms multiple times with different initialization and retain the best combination found. The use of this solution is limited due to its high cost in terms of computation time and the number ...
... optimum), which can in some cases be far from optimal. A naive solution to this problem is to run these algorithms multiple times with different initialization and retain the best combination found. The use of this solution is limited due to its high cost in terms of computation time and the number ...
PDF
... classification is to identify the distinguishing characteristics of predefined classes, based on a set of instances, e.g. students, of each class [13]. Classification is the technique to map a data item into one of several predefined classes. This requires extraction and selection of features that b ...
... classification is to identify the distinguishing characteristics of predefined classes, based on a set of instances, e.g. students, of each class [13]. Classification is the technique to map a data item into one of several predefined classes. This requires extraction and selection of features that b ...
Machine Learning
... • Outlier detection (and removal) – Outliers are unusual data values that are not consistent with most observations which can seriously affect modeling accuracy – Two strategies for dealing with outliers » Removal of outliers » Robust modeling methods • Scaling (normalization), encoding (discretizat ...
... • Outlier detection (and removal) – Outliers are unusual data values that are not consistent with most observations which can seriously affect modeling accuracy – Two strategies for dealing with outliers » Removal of outliers » Robust modeling methods • Scaling (normalization), encoding (discretizat ...
05_iasse_VSSDClust - NDSU Computer Science
... for each cluster. We solve the first two problems based on the concept of being able to formally model the influence of each data point using a function first proposed for DENCLUE [10] and the use of an efficient technique to compute the total influence rapidly over the entire search space. Signific ...
... for each cluster. We solve the first two problems based on the concept of being able to formally model the influence of each data point using a function first proposed for DENCLUE [10] and the use of an efficient technique to compute the total influence rapidly over the entire search space. Signific ...
Food Bytes - CiteSeerX
... ensure that the goods leave the plant at as high a standard as possible, even at the cosmetic level. Using people to visually inspect large numbers of items on a production line is very expensive as well as unreliable, due to finite attention spans and limited visual acuity. Non-visual inspection, s ...
... ensure that the goods leave the plant at as high a standard as possible, even at the cosmetic level. Using people to visually inspect large numbers of items on a production line is very expensive as well as unreliable, due to finite attention spans and limited visual acuity. Non-visual inspection, s ...
Segmentation using decision trees
... • Summary Table (upper left) • Tree-Ring Navigator (upper right) – Accessible from here: Tree Diagram + Assessment Statistics • Assessment Table (lower left) • Assessment Graph (lower right) – blue Training Data, red Validation Data ...
... • Summary Table (upper left) • Tree-Ring Navigator (upper right) – Accessible from here: Tree Diagram + Assessment Statistics • Assessment Table (lower left) • Assessment Graph (lower right) – blue Training Data, red Validation Data ...
Towards a Benchmark for LOD-Enhanced - CEUR
... table with a set of propositions in the form of attribute-value pairs [7]; some structural information is thus lost in aggregations and some relationships in data discarded. Owing to the malleable nature of RDF and flexibility of SPARQL, linked data can be propositionalized via the SPARQL SELECT que ...
... table with a set of propositions in the form of attribute-value pairs [7]; some structural information is thus lost in aggregations and some relationships in data discarded. Owing to the malleable nature of RDF and flexibility of SPARQL, linked data can be propositionalized via the SPARQL SELECT que ...
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.