Vertical Set Square Distance
... Figure 3. Time comparison on 8.3 million rows dataset. The figures show that up to 8.3 million rows both algorithms apparently scale, however VSSD is significantly fast compared to HSSD. It requires only 0.0003 and 0.0004 seconds on average to complete the calculation on each dataset, very much less ...
... Figure 3. Time comparison on 8.3 million rows dataset. The figures show that up to 8.3 million rows both algorithms apparently scale, however VSSD is significantly fast compared to HSSD. It requires only 0.0003 and 0.0004 seconds on average to complete the calculation on each dataset, very much less ...
Text Classification in Data Mining
... There is growing evidence that merging porter stemmer, naive bayes and association rule mining together can produce more efficient and accurate classification systems than traditional classification techniques [26]. Classification is one of the most important tasks in data mining. There are many cla ...
... There is growing evidence that merging porter stemmer, naive bayes and association rule mining together can produce more efficient and accurate classification systems than traditional classification techniques [26]. Classification is one of the most important tasks in data mining. There are many cla ...
icaart 2015 - Munin
... representation and the time series is minimal thus this representation is the best approximation at level k. The image of all the points of the time series on DWT is an n -dimensional vector which we call the ...
... representation and the time series is minimal thus this representation is the best approximation at level k. The image of all the points of the time series on DWT is an n -dimensional vector which we call the ...
CCBD 2016 The 7th International Conference on Cloud Computing
... and classification problems. Multi-objective learning offers not only a novel method to construct and learn ensembles automatically, but also better ways to balance accuracy and diversity in an ensemble. This talk introduces the basic ideas behind multi-objective learning. It describes how ensembles ...
... and classification problems. Multi-objective learning offers not only a novel method to construct and learn ensembles automatically, but also better ways to balance accuracy and diversity in an ensemble. This talk introduces the basic ideas behind multi-objective learning. It describes how ensembles ...
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This article was downloaded by: [Universidad de Chile] Publisher: Routledge
... This article proposes a mechanism for extracting significant information from a deliberation process, which could involve many individuals and an ever greater number of relevant opinions. It is our contention that this mechanism expands the possibilities for deliberation by using computational capac ...
... This article proposes a mechanism for extracting significant information from a deliberation process, which could involve many individuals and an ever greater number of relevant opinions. It is our contention that this mechanism expands the possibilities for deliberation by using computational capac ...
A Hybrid Approach to the Profile Creation and Intrusion Detection
... in an abnormal manner then the actions of that user (or someone who is masquerading as that user) can be classified as intrusive. In these approaches, behaviors can be determined to be abnormal through a comparison against a user profile that represents a user's typical behavior. This user profile, ...
... in an abnormal manner then the actions of that user (or someone who is masquerading as that user) can be classified as intrusive. In these approaches, behaviors can be determined to be abnormal through a comparison against a user profile that represents a user's typical behavior. This user profile, ...
Learning With Constrained and Unlabelled Data
... and j that they should be linked together, i.e., be assigned to the same group. We introduce a binary indicator variable ai,j such that it is 1 if i and j should be in the same group, and 0 otherwise. If the must-link constraints contain no errors, it is natural to assume that the must-link constrai ...
... and j that they should be linked together, i.e., be assigned to the same group. We introduce a binary indicator variable ai,j such that it is 1 if i and j should be in the same group, and 0 otherwise. If the must-link constraints contain no errors, it is natural to assume that the must-link constrai ...
Tweet-based Target Market Classification Using Ensemble Method
... Data mining produces models that can perform consumer trend analysis. A simple data mining process will produce models quickly, but its accuracy will not be quite sufficient. A complex process will produce models that take a long time to execute but will provide results with higher accuracy. However ...
... Data mining produces models that can perform consumer trend analysis. A simple data mining process will produce models quickly, but its accuracy will not be quite sufficient. A complex process will produce models that take a long time to execute but will provide results with higher accuracy. However ...
Improved Apriori Algorithm for Mining Association Rules
... of data from their day to day operations. For example, huge amounts of customer purchase data are collected daily at the checkout counters of grocery stores. Table 1 illustrates an example of such data, commonly known as market basket transactions. Each row in this table corresponds to a transaction ...
... of data from their day to day operations. For example, huge amounts of customer purchase data are collected daily at the checkout counters of grocery stores. Table 1 illustrates an example of such data, commonly known as market basket transactions. Each row in this table corresponds to a transaction ...
A Simple Dimensionality Reduction Technique for Fast Similarity
... Any indexing scheme that does not examine the entire dataset could potentially suffer from two problems, false alarms and false dismissals. False alarms occur when objects that appear to be close in the index are actually distant. Because false alarms can be removed in a post-processing stage (by co ...
... Any indexing scheme that does not examine the entire dataset could potentially suffer from two problems, false alarms and false dismissals. False alarms occur when objects that appear to be close in the index are actually distant. Because false alarms can be removed in a post-processing stage (by co ...
Outlier Detection with Globally Optimal Exemplar
... several groups: (1) statistical or distribution-based approaches; (2) geometric-based approaches; (3) profiling methods; and (4) model-based approaches. In statistical techniques [2, 3], the data points are typically modeled using a data distribution, and points are labeled as outliers depending on ...
... several groups: (1) statistical or distribution-based approaches; (2) geometric-based approaches; (3) profiling methods; and (4) model-based approaches. In statistical techniques [2, 3], the data points are typically modeled using a data distribution, and points are labeled as outliers depending on ...
Distributed Data Mining and Agents
... to another node. This distributed problem solving environment appears to fit very well with the multi-agent framework since the solution requires semi-autonomous behavior, collaboration and reasoning among other things. However, regardless of how sophisticated the agents are, from the domain knowle ...
... to another node. This distributed problem solving environment appears to fit very well with the multi-agent framework since the solution requires semi-autonomous behavior, collaboration and reasoning among other things. However, regardless of how sophisticated the agents are, from the domain knowle ...
PRACTICAL K-ANONYMITY ON LARGE DATASETS By Benjamin
... Unfortunately, releasing data and ensuring anonymity at the same time is very difficult. To keep a user’s identity unknown, it is not enough to strip data of Personally Identifying Information (PII). Even non PII data can still be traced back to a single person if enough personal microdata is provid ...
... Unfortunately, releasing data and ensuring anonymity at the same time is very difficult. To keep a user’s identity unknown, it is not enough to strip data of Personally Identifying Information (PII). Even non PII data can still be traced back to a single person if enough personal microdata is provid ...
Decision Tree Induction: An Approach for Data Classification
... numerical attributes. Classification model construction process performed on huge data, so most decision tree algorithms may result in very bushy or meaningless results. In the worst case, the model cannot be constructed if the size of the data set is too large for the algorithms to handle. Hence, w ...
... numerical attributes. Classification model construction process performed on huge data, so most decision tree algorithms may result in very bushy or meaningless results. In the worst case, the model cannot be constructed if the size of the data set is too large for the algorithms to handle. Hence, w ...
Association Rule Mining -Various Ways: A Comprehensive Study
... data mining. The general concept of association rules is to mine the positive frequent patterns from the overall transaction database. by, mining the negative patterns has drawn the attention of researchers in this sector too. The motive of this survey is to generate latest model for mining interest ...
... data mining. The general concept of association rules is to mine the positive frequent patterns from the overall transaction database. by, mining the negative patterns has drawn the attention of researchers in this sector too. The motive of this survey is to generate latest model for mining interest ...
Z04404159163
... language and has a high learning curve. It has some extensions to that language to use the GPU-specific features that include new API calls, and some new type qualifiers that apply to functions and variables. CUDA has some specific functions, called kernels. A kernel can be a function or a full prog ...
... language and has a high learning curve. It has some extensions to that language to use the GPU-specific features that include new API calls, and some new type qualifiers that apply to functions and variables. CUDA has some specific functions, called kernels. A kernel can be a function or a full prog ...
Multi-relational Bayesian Classification through Genetic
... achieves substantial compactness. To speed up the mining of complete set of rules, CMAR adopts a variant of recently developed FPgrowth method. FP-growth is much faster than Apriori-like methods used in previous association-based classification, such as especially when there exist a huge number of r ...
... achieves substantial compactness. To speed up the mining of complete set of rules, CMAR adopts a variant of recently developed FPgrowth method. FP-growth is much faster than Apriori-like methods used in previous association-based classification, such as especially when there exist a huge number of r ...
Clustering - Computer Science and Engineering
... Similarity of two clusters is based on the two most similar (closest) points in the different clusters – Determined by one pair of points, i.e., by one link in the proximity graph. ...
... Similarity of two clusters is based on the two most similar (closest) points in the different clusters – Determined by one pair of points, i.e., by one link in the proximity graph. ...
A Study on the accessible techniques to classify and predict
... has been used to classify the data and it is evaluated by using 10 fold cross validation. K- Means clustering [16] is a method of cluster analysis which aims to partition „n‟ observation to „k‟ clusters. Euclidean distance formula is used to minimize the sum of square of distance between data. When ...
... has been used to classify the data and it is evaluated by using 10 fold cross validation. K- Means clustering [16] is a method of cluster analysis which aims to partition „n‟ observation to „k‟ clusters. Euclidean distance formula is used to minimize the sum of square of distance between data. When ...