A Model-View-Controller architecture for Knowledge Discovery
... code, business logic code, and presentation code. Such applications are difficult to maintain, because interdependencies between all of the components cause strong ripple effects whenever a change is made anywhere. High coupling makes classes difficult or impossible to reuse because they depend on s ...
... code, business logic code, and presentation code. Such applications are difficult to maintain, because interdependencies between all of the components cause strong ripple effects whenever a change is made anywhere. High coupling makes classes difficult or impossible to reuse because they depend on s ...
Department of Information Technology MCA
... Course objectives: Students undergoing this course are expected to: • Understand the AI problems and techniques. • Know the different heuristic search techniques. • Represent the knowledge in different forms. • Know the different ways of planning and natural language understanding. • Realize the dif ...
... Course objectives: Students undergoing this course are expected to: • Understand the AI problems and techniques. • Know the different heuristic search techniques. • Represent the knowledge in different forms. • Know the different ways of planning and natural language understanding. • Realize the dif ...
PowerPoint - Innovative GIS
... GIS education for the most part insists that non-GIS students interested in understanding map analysis and modeling must be tracked into general GIS courses that are designed for GIS specialists, and material presented primarily focus on commercial GIS software mechanics that GIS-specialists need to ...
... GIS education for the most part insists that non-GIS students interested in understanding map analysis and modeling must be tracked into general GIS courses that are designed for GIS specialists, and material presented primarily focus on commercial GIS software mechanics that GIS-specialists need to ...
Graphical Presentation of Sequential Patterns
... and their parameters are calibrated to optimal values. Typically, there are several techniques for the same data mining problem type. Some techniques have specific requirements on the form of data. Therefore, stepping back to the data preparation phase is often needed. Evaluation At this stage in th ...
... and their parameters are calibrated to optimal values. Typically, there are several techniques for the same data mining problem type. Some techniques have specific requirements on the form of data. Therefore, stepping back to the data preparation phase is often needed. Evaluation At this stage in th ...
Proceedings of the 21st Australasian Joint Conference on Artificial
... The concept of Interchangeability was developed to deal with redundancy of values in the same domain. Conventional algorithms for detecting Neighborhood Interchangeability work by gradually establishing relationships between values from scratch. We propose ... The concept of Interchangeability was d ...
... The concept of Interchangeability was developed to deal with redundancy of values in the same domain. Conventional algorithms for detecting Neighborhood Interchangeability work by gradually establishing relationships between values from scratch. We propose ... The concept of Interchangeability was d ...
Predicting the need for vehicle compressor repairs using
... et al. (2007) discuss fault prognostics, after-sales service and warranty claims. Two representative examples of work in this area are Buddhakulsomsiri and Zakarian (2009) and Rajpathak (2013). Buddhakulsomsiri and Zakarian (2009) present a data mining algorithm that extracts associative and sequent ...
... et al. (2007) discuss fault prognostics, after-sales service and warranty claims. Two representative examples of work in this area are Buddhakulsomsiri and Zakarian (2009) and Rajpathak (2013). Buddhakulsomsiri and Zakarian (2009) present a data mining algorithm that extracts associative and sequent ...
PDF
... In this section, we review the existing work on privacypreserving data mining and differential privacy. One important topic to protect the sensitive information during the procedures of data analysis is privacy-preserving data mining (Aggarwal and Yu 2008). To do so, the owners of sensitive data usu ...
... In this section, we review the existing work on privacypreserving data mining and differential privacy. One important topic to protect the sensitive information during the procedures of data analysis is privacy-preserving data mining (Aggarwal and Yu 2008). To do so, the owners of sensitive data usu ...
Comparative Analysis of Various Clustering Algorithms
... and then using the probabilities associated with x can be used to find weighted average over the entire set of data points. The resulting algorithm is commonly called soft EM. The counts used to compute these weighted averages are called soft counts (as opposed to the hard counts used in a hard-EM-t ...
... and then using the probabilities associated with x can be used to find weighted average over the entire set of data points. The resulting algorithm is commonly called soft EM. The counts used to compute these weighted averages are called soft counts (as opposed to the hard counts used in a hard-EM-t ...
Data Mining in Bioinformatics Day 4: Text Mining
... 1 item = candidates Ck+1 for being frequent k+1 itemsets 3. Check the frequency of these candidates Ck+1: the frequent ones form the frequent k + 1-itemsets (trick: discard any candidate immediately that contains an infrequent k -itemset) 4. Repeat from Step 2 until no more candidate is frequent Kar ...
... 1 item = candidates Ck+1 for being frequent k+1 itemsets 3. Check the frequency of these candidates Ck+1: the frequent ones form the frequent k + 1-itemsets (trick: discard any candidate immediately that contains an infrequent k -itemset) 4. Repeat from Step 2 until no more candidate is frequent Kar ...
Major Research Challenges in Data Mining
... and structured patterns from large data, but clearly, more needs to be done. Another form of complexity is from data that are non-i.i.d. (independent and identically distributed). This problem can occur when mining data from multiple relations. In most domains, the objects of interest are not indepe ...
... and structured patterns from large data, but clearly, more needs to be done. Another form of complexity is from data that are non-i.i.d. (independent and identically distributed). This problem can occur when mining data from multiple relations. In most domains, the objects of interest are not indepe ...
a two-staged clustering algorithm for multiple scales
... meaning a high intra-class similarity and a low inter-class similarity. The quality of a clustering method is also measured by its ability to discover hidden patterns [1]. There are two kinds of clustering methods -- hierarchical and partitioning. This study used a k-means method (one of the popular ...
... meaning a high intra-class similarity and a low inter-class similarity. The quality of a clustering method is also measured by its ability to discover hidden patterns [1]. There are two kinds of clustering methods -- hierarchical and partitioning. This study used a k-means method (one of the popular ...
An Analysis on Multi-Agent Based Distributed Data Mining System
... fundamental problem is to compare local theories with previously unseen data, i.e. other agent's data. This data is of course summarized by the results produced by the other agents. Both theory revision and knowledge integration as resources in helping to produce a single global result is taken care ...
... fundamental problem is to compare local theories with previously unseen data, i.e. other agent's data. This data is of course summarized by the results produced by the other agents. Both theory revision and knowledge integration as resources in helping to produce a single global result is taken care ...
The Ten Most Common Data Mining Business Mistakes
... There are many possible reasons data owners withhold data, and it only takes one to stop a data mining project dead in its tracks. Data scientists need both timely access to data and good information about the data. They need to know how it is collected and maintained, why it is messy and/or incomp ...
... There are many possible reasons data owners withhold data, and it only takes one to stop a data mining project dead in its tracks. Data scientists need both timely access to data and good information about the data. They need to know how it is collected and maintained, why it is messy and/or incomp ...
Text Documents Clustering
... K-means algorithm with cosine similarity have reached almost the same values of F1 and incorrect clustered documents but despite small difference between F1 values and evaluating the time taken for clustering (Table V), K-means with cosine similarity results can be considered well because of conside ...
... K-means algorithm with cosine similarity have reached almost the same values of F1 and incorrect clustered documents but despite small difference between F1 values and evaluating the time taken for clustering (Table V), K-means with cosine similarity results can be considered well because of conside ...
Algorithmic Bias from discrimination discovery to fairness
... [Pre_2] F. Kamiran and T. Calders. “Data preprocessing techniques for classification without discrimination”. In Knowledge and Information Systems (KAIS), 33(1), 2012. [Pre_3] S. Hajian and J. Domingo-Ferrer. “A methodology for direct and indirect discrimination prevention in data mining”. In IEEE T ...
... [Pre_2] F. Kamiran and T. Calders. “Data preprocessing techniques for classification without discrimination”. In Knowledge and Information Systems (KAIS), 33(1), 2012. [Pre_3] S. Hajian and J. Domingo-Ferrer. “A methodology for direct and indirect discrimination prevention in data mining”. In IEEE T ...
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.