Statistics and Machine Learning at Scale
... Thompson, “To measure whether or not you’re improving performance, you look at an objective function, such as minimizing a loss function. The algorithm iterates through the data until a convergence criterion is met. You typically use holdout data to see if you are overfitting.” ...
... Thompson, “To measure whether or not you’re improving performance, you look at an objective function, such as minimizing a loss function. The algorithm iterates through the data until a convergence criterion is met. You typically use holdout data to see if you are overfitting.” ...
A Framework of Business Intelligence
... computing systems and tremendous amounts of data stored in databases. With the growth of demand, situational service that is comprised of two or more disparate e-Services are required, which have been combined to create a new integrated experience. As such, e-Service integration eventually becomes a ...
... computing systems and tremendous amounts of data stored in databases. With the growth of demand, situational service that is comprised of two or more disparate e-Services are required, which have been combined to create a new integrated experience. As such, e-Service integration eventually becomes a ...
P6-ch18-data_analysis_and_mining
... The earliest OLAP systems used multidimensional arrays in memory to store data cubes (ie, Programming Language’s Array Data Type) ...
... The earliest OLAP systems used multidimensional arrays in memory to store data cubes (ie, Programming Language’s Array Data Type) ...
The WEKA data mining software: an update
... The second panel in the Explorer gives access to WEKA’s classification and regression algorithms. The corresponding panel is called “Classify” because regression techniques are viewed as predictors of “continuous classes”. By default, the panel runs a cross-validation for a selected learning algorit ...
... The second panel in the Explorer gives access to WEKA’s classification and regression algorithms. The corresponding panel is called “Classify” because regression techniques are viewed as predictors of “continuous classes”. By default, the panel runs a cross-validation for a selected learning algorit ...
Decision Support System for the Stock Market using Data Analytics
... dynamic system. It is a popular investment platform that appeals to a wide variety of masses. While the stock market remains a significant way to earn profit, it is often considered one of the most risky forms of investment due to the underlying nature of the financial domain and a host of various f ...
... dynamic system. It is a popular investment platform that appeals to a wide variety of masses. While the stock market remains a significant way to earn profit, it is often considered one of the most risky forms of investment due to the underlying nature of the financial domain and a host of various f ...
Using Probabilistic Latent Semantic Analysis for Web Page Grouping
... high-dimensional matrix. This is mainly because that there is usually tens to hundreds of thousands sessions in web log files. Consequently, the high computational difficulty will be incurred in when we utilize sessions as dimensions rather than pages, on which we will employ clustering technique. A ...
... high-dimensional matrix. This is mainly because that there is usually tens to hundreds of thousands sessions in web log files. Consequently, the high computational difficulty will be incurred in when we utilize sessions as dimensions rather than pages, on which we will employ clustering technique. A ...
Title A Multi-Agent System for Context
... model of all consumers from various shops, even though the base learner is accurate at the site where it is created. The more variance the context has, the less accuracy can be obtained by using a base learner to represent the real model. If base learners are not accurate, the accuracy of the final ...
... model of all consumers from various shops, even though the base learner is accurate at the site where it is created. The more variance the context has, the less accuracy can be obtained by using a base learner to represent the real model. If base learners are not accurate, the accuracy of the final ...
E-Learning Using Data Mining
... field of research, it is almost contemporary to e-learning. It is, though, rather difficult to define. Not because of its intrinsic complexity, but because it has most of its roots in the ever-shifting world of business. At its most detailed, it can be understood not just as a collection of data ana ...
... field of research, it is almost contemporary to e-learning. It is, though, rather difficult to define. Not because of its intrinsic complexity, but because it has most of its roots in the ever-shifting world of business. At its most detailed, it can be understood not just as a collection of data ana ...
Recursive information granulation
... In this paper, we are concerned with information granules and information granulation carried out in the setting of set theory and interval analysis. The rationale behind a selection of this formal framework is twofold. First, interval analysis has been around as one of the cornerstones of granular ...
... In this paper, we are concerned with information granules and information granulation carried out in the setting of set theory and interval analysis. The rationale behind a selection of this formal framework is twofold. First, interval analysis has been around as one of the cornerstones of granular ...
A Rule-Based Classification Algorithm for Uncertain Data
... missing attribute values. However, the problem studied in this paper is different from before. Instead of assuming part of the data has missing or noisy values, we allow the whole dataset to be uncertain. Furthermore, the uncertainty is not shown as missing or erroneous values but represented as unc ...
... missing attribute values. However, the problem studied in this paper is different from before. Instead of assuming part of the data has missing or noisy values, we allow the whole dataset to be uncertain. Furthermore, the uncertainty is not shown as missing or erroneous values but represented as unc ...
talk
... Vipin Kumar performed an extensive empirical evaluation and noted that “..on 19 different publicly available data sets, comparing 9 different techniques (time series discords) is the best overall technique.”. V. Chandola, D. Cheboli, V. Kumar. Detecting Anomalies in a Time Series Database. UMN TR09- ...
... Vipin Kumar performed an extensive empirical evaluation and noted that “..on 19 different publicly available data sets, comparing 9 different techniques (time series discords) is the best overall technique.”. V. Chandola, D. Cheboli, V. Kumar. Detecting Anomalies in a Time Series Database. UMN TR09- ...
Multi-Label Classification: An Overview
... can belong to different levels of the hierarchy. The top level of the MIPS (Munich Information Centre for Protein Sequences) hierarchy (http://mips.gsf.de/) consists of classes such as: Metabolism, Energy, Transcription and Protein Synthesis. Each of these classes is then subdivided into more specif ...
... can belong to different levels of the hierarchy. The top level of the MIPS (Munich Information Centre for Protein Sequences) hierarchy (http://mips.gsf.de/) consists of classes such as: Metabolism, Energy, Transcription and Protein Synthesis. Each of these classes is then subdivided into more specif ...
Finding Frequent and Maximal Periodic Patterns in
... within time intervals. In general, there are three types of periodic patterns can be detected in a time series Database such as Symbol Periodicity, Sequence Periodicity or Partial Periodic Patterns and Segment or Full-Cycle Periodicity [22]. We consider a set of Boolean SpatioTemporal (ST) event typ ...
... within time intervals. In general, there are three types of periodic patterns can be detected in a time series Database such as Symbol Periodicity, Sequence Periodicity or Partial Periodic Patterns and Segment or Full-Cycle Periodicity [22]. We consider a set of Boolean SpatioTemporal (ST) event typ ...
- Courses - University of California, Berkeley
... • Data Understanding – Explore the data and verify the quality ...
... • Data Understanding – Explore the data and verify the quality ...
Steven F. Ashby Center for Applied Scientific Computing
... to each other based on the important terms appearing in them. Approach: To identify frequently occurring terms in each document. Form a similarity measure based on the frequencies of different terms. Use it to cluster. Gain: Information Retrieval can utilize the clusters to relate a new document ...
... to each other based on the important terms appearing in them. Approach: To identify frequently occurring terms in each document. Form a similarity measure based on the frequencies of different terms. Use it to cluster. Gain: Information Retrieval can utilize the clusters to relate a new document ...
M.Tech. (Full Time)
... This course gives a comprehensive coverage of algorithms specially meant for analyzing data at an in-depth level. Decision trees, Support Vector machines and Neural networks are considered to be highly effective in analyzing complex data. INSTRUCTIONAL OBJECTIVES ...
... This course gives a comprehensive coverage of algorithms specially meant for analyzing data at an in-depth level. Decision trees, Support Vector machines and Neural networks are considered to be highly effective in analyzing complex data. INSTRUCTIONAL OBJECTIVES ...
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.