From Sound to “Sense” via Feature Extraction and Machine Learning
... of an appropriate style label to a piece of music, has become a popular benchmark task in the MIR community (for many reasons, not the least of them being the fact that genre labels are generally much easier to obtain than other, more intuitive or personal descriptors). Accordingly, the current chap ...
... of an appropriate style label to a piece of music, has become a popular benchmark task in the MIR community (for many reasons, not the least of them being the fact that genre labels are generally much easier to obtain than other, more intuitive or personal descriptors). Accordingly, the current chap ...
Vessel pattern knowledge discovery from AIS data: a - NATO-CMRE
... explicitly deriving turning points. As will be seen, it is still possible to consistently capture maritime patterns in a compact and accurate way. It is also feasible to extract temporal information, like route travel time distributions and daily patterns, as well as to associate historical route pa ...
... explicitly deriving turning points. As will be seen, it is still possible to consistently capture maritime patterns in a compact and accurate way. It is also feasible to extract temporal information, like route travel time distributions and daily patterns, as well as to associate historical route pa ...
published version - GeoAnalytics.net
... designed, for example, by amending one of the existing tools. As we have discussed, a promising approach is combining data aggregation with data filtering, i.e. aggregated representation of the entire set and subsets (classes) with a superimposed display of individual characteristics of user-selecte ...
... designed, for example, by amending one of the existing tools. As we have discussed, a promising approach is combining data aggregation with data filtering, i.e. aggregated representation of the entire set and subsets (classes) with a superimposed display of individual characteristics of user-selecte ...
Slide 1
... TrustRank: topic-specific PageRank with a teleport set of “trusted” pages SimRank: measure similarity between items a k-partite graph with k types of nodes Example: picture nodes and tag nodes ...
... TrustRank: topic-specific PageRank with a teleport set of “trusted” pages SimRank: measure similarity between items a k-partite graph with k types of nodes Example: picture nodes and tag nodes ...
Data Mining
... Cross-market analysis—Find associations/co-relations between product sales, & predict based on such association Customer profiling—What types of customers buy what products (clustering or classification) ...
... Cross-market analysis—Find associations/co-relations between product sales, & predict based on such association Customer profiling—What types of customers buy what products (clustering or classification) ...
- Staffordshire University
... the installation, may need to be installed separately. For two reasons, however, we decided against using AdventureWorks. There are a large number of online tutorials written explicitly for the AdventureWorks database. Using a different data set for classroom teaching meant that the AdventureWorks t ...
... the installation, may need to be installed separately. For two reasons, however, we decided against using AdventureWorks. There are a large number of online tutorials written explicitly for the AdventureWorks database. Using a different data set for classroom teaching meant that the AdventureWorks t ...
Missing Value Imputation in Multi Attribute Data Set
... Replace a missing data with the mean (numeric attribute) or mode (nominal attribute) of all cases observed. To reduce the influence of exceptional data, median can also be used. This is one of the most common used methods [5]. K-Nearest Neighbor Imputation (KNN) This method uses k-nearest neighbor a ...
... Replace a missing data with the mean (numeric attribute) or mode (nominal attribute) of all cases observed. To reduce the influence of exceptional data, median can also be used. This is one of the most common used methods [5]. K-Nearest Neighbor Imputation (KNN) This method uses k-nearest neighbor a ...
05-Trajectory Pattern Mining
... Concept – Patterns on future trajectories of objects, assuming that the objects keep their current speeds and directions. ...
... Concept – Patterns on future trajectories of objects, assuming that the objects keep their current speeds and directions. ...
Bridging Information Visualization with Machine Learning
... such as feature selection, dimensionality reduction and clustering. Here we describe additional machine learning concepts that may be of benefit for visualization research. Binary hashing. Binary hashing has emerged in recent years as an efficient way to speed up information retrieval of high-dimens ...
... such as feature selection, dimensionality reduction and clustering. Here we describe additional machine learning concepts that may be of benefit for visualization research. Binary hashing. Binary hashing has emerged in recent years as an efficient way to speed up information retrieval of high-dimens ...
Slides - Microsoft
... Concept – Patterns on future trajectories of objects, assuming that the objects keep their current speeds and directions. ...
... Concept – Patterns on future trajectories of objects, assuming that the objects keep their current speeds and directions. ...
On Integrating Information Visualization Techniques into Data
... producing output of graphic format based on original structure of data which usually involves human sense making[90] to further the analysis. Data mining, on the other hand, use automatic algorithmic techniques in such analysis process, to discover derived structure of data or directly provide off t ...
... producing output of graphic format based on original structure of data which usually involves human sense making[90] to further the analysis. Data mining, on the other hand, use automatic algorithmic techniques in such analysis process, to discover derived structure of data or directly provide off t ...
Data Mining and Predictive Modeling in Institutional Advancement
... After determining variables in its model, A&S prepared the possible predictors. For nominal variables, this meant converting each value into a new variable with two possible values: 1 for Yes and 0 for No. Some continuous variables, such as “Graduation Year” and “Lifetime Giving,” were transformed a ...
... After determining variables in its model, A&S prepared the possible predictors. For nominal variables, this meant converting each value into a new variable with two possible values: 1 for Yes and 0 for No. Some continuous variables, such as “Graduation Year” and “Lifetime Giving,” were transformed a ...
Visual Methods for Analyzing Time-Oriented Data
... into larger datasets. This is the point where analytical methods come into play. In Section III, we will illustrate (again by examples) the usefulness of combining visual and analytical methods particularly related to time-oriented data. In order to achieve better targeted applications, users and th ...
... into larger datasets. This is the point where analytical methods come into play. In Section III, we will illustrate (again by examples) the usefulness of combining visual and analytical methods particularly related to time-oriented data. In order to achieve better targeted applications, users and th ...
for Literature Analysis Science Navigation Map: An Interactive Data
... topics, etc. On another hand, exploding volume of research papers and complex multi-wise relations between papers bring great challenges for scientists to accurately identify important and relevant research papers as well as explore and analyse papers. To address these challenges, researchers have p ...
... topics, etc. On another hand, exploding volume of research papers and complex multi-wise relations between papers bring great challenges for scientists to accurately identify important and relevant research papers as well as explore and analyse papers. To address these challenges, researchers have p ...
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.