Research Statement
... time, provide strong security guarantees based on the formal security definition from graph encryption. The first scheme is computationally efficient, while the second one is communication-efficient by adopting specialized homomorphic encryption schemes. I also propose a third scheme which is both c ...
... time, provide strong security guarantees based on the formal security definition from graph encryption. The first scheme is computationally efficient, while the second one is communication-efficient by adopting specialized homomorphic encryption schemes. I also propose a third scheme which is both c ...
Apriori Algorithm
... their own database as well. There are some distributed algorithms, which are not based on Apriori, for instance [5] contributes such an algorithm. The paper [6] shows a detailed analysis of the Apriori algorithm and it concluded that the finding the 2 frequent itemsets is the significant part of the ...
... their own database as well. There are some distributed algorithms, which are not based on Apriori, for instance [5] contributes such an algorithm. The paper [6] shows a detailed analysis of the Apriori algorithm and it concluded that the finding the 2 frequent itemsets is the significant part of the ...
Using Projections to Visually Cluster High
... concise and interpretable information within that data—is called knowledge discovery in databases (KDD). Data mining refers to one specific step in the KDD process— namely, to the application of algorithms that can extract hidden patterns from data (see the “KDD Process” sidebar for more information ...
... concise and interpretable information within that data—is called knowledge discovery in databases (KDD). Data mining refers to one specific step in the KDD process— namely, to the application of algorithms that can extract hidden patterns from data (see the “KDD Process” sidebar for more information ...
Full-Text - International Journal of Computer Science Issues
... huge amount of text data available in internet, news, institutes , To make an effective text classifier we need large amount of labeled data in the form of training samples, to get labeled data is not only expensive but also time consuming, tedious task, whereas unlabelled data is easily available & ...
... huge amount of text data available in internet, news, institutes , To make an effective text classifier we need large amount of labeled data in the form of training samples, to get labeled data is not only expensive but also time consuming, tedious task, whereas unlabelled data is easily available & ...
Accuracy vs. Comprehensibility in Data Mining
... overall model. The motivation for using ensembles in general is obvious; they are more robust, i.e. their applicability span over a larger set of problems. At the same time an ensemble is by definition a set of models, making it very hard to express the relationships found in original variables. The ...
... overall model. The motivation for using ensembles in general is obvious; they are more robust, i.e. their applicability span over a larger set of problems. At the same time an ensemble is by definition a set of models, making it very hard to express the relationships found in original variables. The ...
Notes for Lect 9 - rci.rutgers.edu
... Grow a "random" tree, where at each node, the best split is chosen among mtry randomly selected variables. The tree is grown to maximum size and not pruned back. Use the tree to predict out-of-bag data. ...
... Grow a "random" tree, where at each node, the best split is chosen among mtry randomly selected variables. The tree is grown to maximum size and not pruned back. Use the tree to predict out-of-bag data. ...
2012-13
... Monotonic vs. static schedules), Real-world issues: blocking, unpredictability, interrupts, caching, examples of OS’ s for embedded systems (RT Linux/ VRT), selected case studies. ...
... Monotonic vs. static schedules), Real-world issues: blocking, unpredictability, interrupts, caching, examples of OS’ s for embedded systems (RT Linux/ VRT), selected case studies. ...
Different Data Mining Techniques And Clustering Algorithms
... and complexity of these two algorithmks. With the help of these two algorithms it is possible to extend our space and similarity between the data sets present each nodes. It helps in increasing the possibility to differentiate the dissimilarity among the cluster nodes. The best area to apply this co ...
... and complexity of these two algorithmks. With the help of these two algorithms it is possible to extend our space and similarity between the data sets present each nodes. It helps in increasing the possibility to differentiate the dissimilarity among the cluster nodes. The best area to apply this co ...
A Baseline Method for Genealogical Entity
... The former have a structured form, but the latter consist mostly of free text, and as such are qualitatively different. For a given set of notary acts, we aim at identifying all persons involved and link them to their birth, marriage and death certificates. The goal of this paper is to investigate ...
... The former have a structured form, but the latter consist mostly of free text, and as such are qualitatively different. For a given set of notary acts, we aim at identifying all persons involved and link them to their birth, marriage and death certificates. The goal of this paper is to investigate ...
A Review of Data Mining Classification Techniques Applied
... modeling large amounts of data in order to discover unknown patterns or relationships which provide a clear and useful result to the data analyst [2]. There are two types of data mining tasks: descriptive data mining tasks that describe the general properties of the existing data, and predictive dat ...
... modeling large amounts of data in order to discover unknown patterns or relationships which provide a clear and useful result to the data analyst [2]. There are two types of data mining tasks: descriptive data mining tasks that describe the general properties of the existing data, and predictive dat ...
Density-based Cluster Algorithms in Low
... was quantified by the F -measure, which combines the achieved precision- and recall-values relative to the 10 classes.5 Figure 7 shows the achieved classification performance: The x-axis indicates the number of dimensions of the embedding space (from 2 to 13), the y-axis indicates the F -measure val ...
... was quantified by the F -measure, which combines the achieved precision- and recall-values relative to the 10 classes.5 Figure 7 shows the achieved classification performance: The x-axis indicates the number of dimensions of the embedding space (from 2 to 13), the y-axis indicates the F -measure val ...
Learning Bregman Distance Functions and Its Application
... aim to learn a full matrix for the target distance metric that is in the square of the dimensionality, making it computationally unattractive for high dimensional data. Although the computation can be reduced significantly by assuming certain forms of the distance metric (e.g., diagonal matrix), the ...
... aim to learn a full matrix for the target distance metric that is in the square of the dimensionality, making it computationally unattractive for high dimensional data. Although the computation can be reduced significantly by assuming certain forms of the distance metric (e.g., diagonal matrix), the ...
Survey of E-Learning: Content Personalization
... mistakes. And than those who were not and remained busy towards lengthier on the site.The use of data mining results shown that allow hints as division of the system loop is very effective. Intelligent systems on very much less of the time and cost expenses related with traditional ITSs. Cristina Ca ...
... mistakes. And than those who were not and remained busy towards lengthier on the site.The use of data mining results shown that allow hints as division of the system loop is very effective. Intelligent systems on very much less of the time and cost expenses related with traditional ITSs. Cristina Ca ...
X24164167
... offer corresponding service for different card-rank users. From this way we can enhance customers' loyalty to the store. Therefore, so as to recommend corresponding card to the appropriate customer, we want to obtain different cardrank customers’ characteristics and which is the most important facto ...
... offer corresponding service for different card-rank users. From this way we can enhance customers' loyalty to the store. Therefore, so as to recommend corresponding card to the appropriate customer, we want to obtain different cardrank customers’ characteristics and which is the most important facto ...
The Data Warehouse and Business Intelligence
... that provides users with access to consolidated, historic, or static data extracted from operational databases, usually augmented with external data The Data Warehouse (DW) and Business Intelligence (BI) 9.5 ...
... that provides users with access to consolidated, historic, or static data extracted from operational databases, usually augmented with external data The Data Warehouse (DW) and Business Intelligence (BI) 9.5 ...
Discovering Web Document Clusters with Self
... [10]. This data set contains 20,000 UseNet news postings having the form of email messages. The 20,000 messages were collected randomly from 20 different Netnews newsgroups, 1000 messages from each newsgroup [10]. The data set is “labelled”, by being already partitioned into twenty categories. This ...
... [10]. This data set contains 20,000 UseNet news postings having the form of email messages. The 20,000 messages were collected randomly from 20 different Netnews newsgroups, 1000 messages from each newsgroup [10]. The data set is “labelled”, by being already partitioned into twenty categories. This ...
Large Datasets in Biomedicine: A Discussion of Salient Analytic Issues
... The term “large dataset” was used academically as early as 1975, when the first conference on large datasets was held (http://portal.acm.org/toc.cfm?id⫽1282480) discussing database design and management. Some of the characteristics of large datasets, such as high dimensionality (see definition on pa ...
... The term “large dataset” was used academically as early as 1975, when the first conference on large datasets was held (http://portal.acm.org/toc.cfm?id⫽1282480) discussing database design and management. Some of the characteristics of large datasets, such as high dimensionality (see definition on pa ...
Two-level Clustering Approach to Training Data Instance Selection
... most necessary. For example, in the steel industry common products are made daily and the model to plan the production settings is not used, but in the production of rare cases the model is generally needed. Thus, leaving out the rare cases from the model training data would in the worst case lead t ...
... most necessary. For example, in the steel industry common products are made daily and the model to plan the production settings is not used, but in the production of rare cases the model is generally needed. Thus, leaving out the rare cases from the model training data would in the worst case lead 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.