Slides
... Pseudocode for 1R For each attribute, For each value of the attribute, make a rule as follows: count how often each class appears find the most frequent class make the rule assign that class to this attribute-value Calculate the error rate of the rules Choose the rules with the smallest error rate ...
... Pseudocode for 1R For each attribute, For each value of the attribute, make a rule as follows: count how often each class appears find the most frequent class make the rule assign that class to this attribute-value Calculate the error rate of the rules Choose the rules with the smallest error rate ...
new methods for mining sequential and time series data
... an approach to mine and query large ST data with the aim of finding interesting patterns and understanding the underlying process of data generation. An important class of queries is based on the flock pattern. A flock is a large subset of objects moving ...
... an approach to mine and query large ST data with the aim of finding interesting patterns and understanding the underlying process of data generation. An important class of queries is based on the flock pattern. A flock is a large subset of objects moving ...
Mining Subgroups with Exceptional Transition Behavior
... subgroups. As a canonical choice, we focus in our experiments on conjunctions of selection conditions over individual describing attributes, i.e., attribute-value pairs in the case of a nominal attribute, or intervals in the case of numeric attributes. Hence, an example description of a subgroup cou ...
... subgroups. As a canonical choice, we focus in our experiments on conjunctions of selection conditions over individual describing attributes, i.e., attribute-value pairs in the case of a nominal attribute, or intervals in the case of numeric attributes. Hence, an example description of a subgroup cou ...
Geospatial Big Data Handling Theory and Methods: A
... sensors. Cars (e.g., connected vehicles) are equipped with many sensors to aid the driver and enhance safety and comfort. These sensors also capture the immediate environment of the car using front cameras, backwards cameras, ultrasonic (for parking assistance), GPS, radar, rain-sensing wipers (Fitz ...
... sensors. Cars (e.g., connected vehicles) are equipped with many sensors to aid the driver and enhance safety and comfort. These sensors also capture the immediate environment of the car using front cameras, backwards cameras, ultrasonic (for parking assistance), GPS, radar, rain-sensing wipers (Fitz ...
Segmentation, Classification, and Clustering of Temporal Data
... geophysics, engineering, and quantitative finance. In general, a time series is a sequence of data points, measured at successive points in time and spaced at uniform time intervals. This thesis is concerned with time series mining, including segmentation, classification, and clustering of temporal ...
... geophysics, engineering, and quantitative finance. In general, a time series is a sequence of data points, measured at successive points in time and spaced at uniform time intervals. This thesis is concerned with time series mining, including segmentation, classification, and clustering of temporal ...
Trend Mining for Predictive Product Design
... There have been several data mining algorithms proposed to address continuously changing data streams. For example, the very fast decision tree (VFDT) learner employs the Hoeffding statistic to build a decision tree classifier that has similar predictive characteristics as a conventional decision tre ...
... There have been several data mining algorithms proposed to address continuously changing data streams. For example, the very fast decision tree (VFDT) learner employs the Hoeffding statistic to build a decision tree classifier that has similar predictive characteristics as a conventional decision tre ...
A Study on Market Basket Analysis using Data
... decrease rapidly. It's time & space cost increases not drastically when data-set volume increases, so its usability retains for MFI applications for high volume data-sets. The DCIP algorithm can be further optimized in various aspects, such as keep a record of all resulting intersections to avoid du ...
... decrease rapidly. It's time & space cost increases not drastically when data-set volume increases, so its usability retains for MFI applications for high volume data-sets. The DCIP algorithm can be further optimized in various aspects, such as keep a record of all resulting intersections to avoid du ...
Application Of Data Mining Technology To Support Fraud Protection
... Next to this I would like to express my sincerest gratitude and heartfelt thanks to my advisor, Dr. Dereje Teferi. I am really grateful for his constructive comments and critical readings of the study. I am very thankful to Dr. Million Meshesha for his support. I am also very thankful to my instruct ...
... Next to this I would like to express my sincerest gratitude and heartfelt thanks to my advisor, Dr. Dereje Teferi. I am really grateful for his constructive comments and critical readings of the study. I am very thankful to Dr. Million Meshesha for his support. I am also very thankful to my instruct ...
Big Data Meets Text Mining
... key computational algorithms in a specific grid environment, and now it contains many algorithms that can run on a variety of hosts and grid environments. In 2012, SAS High-Performance Analytics Server 12.2 added highperformance text mining to the project. SAS high-performance text mining provides f ...
... key computational algorithms in a specific grid environment, and now it contains many algorithms that can run on a variety of hosts and grid environments. In 2012, SAS High-Performance Analytics Server 12.2 added highperformance text mining to the project. SAS high-performance text mining provides f ...
Anonymizing Transaction Databases for Publication
... no structure and can be extremely high dimensional. Traditional anonymization methods lose too much information on such data. To date, there has been no satisfactory privacy notion and solution proposed for anonymizing transaction data. This paper proposes one way to address this issue. ...
... no structure and can be extremely high dimensional. Traditional anonymization methods lose too much information on such data. To date, there has been no satisfactory privacy notion and solution proposed for anonymizing transaction data. This paper proposes one way to address this issue. ...
distributed data mining and agent mining interaction and integration
... referred by the acronym DDM) considers data mining in this broader context. DDM may also be useful in environments with multiple compute nodes connected over high speed networks. Even if the data can be quickly centralized using the relatively fast network, proper balancing of computational load amo ...
... referred by the acronym DDM) considers data mining in this broader context. DDM may also be useful in environments with multiple compute nodes connected over high speed networks. Even if the data can be quickly centralized using the relatively fast network, proper balancing of computational load amo ...
Course on Data Mining
... What is Data Mining? • Ultimately: – "Extraction of interesting (non-trivial, implicit, previously unknown, potentially useful) information or patterns from data in large databases" ...
... What is Data Mining? • Ultimately: – "Extraction of interesting (non-trivial, implicit, previously unknown, potentially useful) information or patterns from data in large databases" ...
Effective Algorithms for Sequential Pattern Mining
... mining research because it is the basis of many applications, such as customer behavior analysis, stock trend prediction, and DNA sequence analysis. The sequential mining problem was first introduced in [4]. From then on, much work has been carried out on mining frequent patterns, as for example, in ...
... mining research because it is the basis of many applications, such as customer behavior analysis, stock trend prediction, and DNA sequence analysis. The sequential mining problem was first introduced in [4]. From then on, much work has been carried out on mining frequent patterns, as for example, in ...
Mining Databases on the Web
... key information from the data and then mine the extracted information. This is not different from what we have mentioned in our previous books (for example, see [THUR98]). However, because Web data may be coming from numerous sources, it may be incomplete or inconsistent. Therefore, we will have to ...
... key information from the data and then mine the extracted information. This is not different from what we have mentioned in our previous books (for example, see [THUR98]). However, because Web data may be coming from numerous sources, it may be incomplete or inconsistent. Therefore, we will have to ...
Contents - Computer Science
... Getting back to your task at AllElectronics, suppose that you would like to include data from multiple sources in your analysis. This would involve integrating multiple databases, data cubes, or les, i.e., data integration. Yet some attributes representing a given concept may have di erent names in ...
... Getting back to your task at AllElectronics, suppose that you would like to include data from multiple sources in your analysis. This would involve integrating multiple databases, data cubes, or les, i.e., data integration. Yet some attributes representing a given concept may have di erent names in ...
Flexible Frameworks for Actionable Knowledge Discovery
... business processes and systems. If that is the case, data mining has good potential to lead to productivity gain, smarter operation, and decision making in business intelligence. Such efforts actually aim at the KDD paradigm shift from traditionally technical interestingness-oriented and datacentere ...
... business processes and systems. If that is the case, data mining has good potential to lead to productivity gain, smarter operation, and decision making in business intelligence. Such efforts actually aim at the KDD paradigm shift from traditionally technical interestingness-oriented and datacentere ...
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.