Association rule mining
... Clearly the space of all association rules is exponential, O(2m), where m is the number of items in I. The mining exploits sparseness of data, and high minimum support and high minimum confidence values. Still, it always produces a huge number of rules, thousands, tens of thousands, millions, ...
... Clearly the space of all association rules is exponential, O(2m), where m is the number of items in I. The mining exploits sparseness of data, and high minimum support and high minimum confidence values. Still, it always produces a huge number of rules, thousands, tens of thousands, millions, ...
Big Data Opportunities and Challenges: Discussions from Data
... also for the concern of using only part of the data, remain an interesting but under-explored area of research. Another way to check the validity of the analysis results is to derive interpretable models. Although many machine learning models are black-boxes, there have been studies on improving the ...
... also for the concern of using only part of the data, remain an interesting but under-explored area of research. Another way to check the validity of the analysis results is to derive interpretable models. Although many machine learning models are black-boxes, there have been studies on improving the ...
On Cluster Tree for Nested and Multi
... density data sets. Karypis et al.[3] proposed and developed a hierarchical clustering algorithm (CHAMELEON) to measure the similarity of two clusters based on a dynamic model. In the clustering process, two clusters are merged only if the inter⇽connectivity and proximity between two clusters are hig ...
... density data sets. Karypis et al.[3] proposed and developed a hierarchical clustering algorithm (CHAMELEON) to measure the similarity of two clusters based on a dynamic model. In the clustering process, two clusters are merged only if the inter⇽connectivity and proximity between two clusters are hig ...
Efficient storage and querying of sequential patterns in - delab-auth
... collections of sets was also analyzed by Kitagawa, Ishikawa, and Obho [18]. In Ref. [24] a set-based bitmap index is presented, which facilitates the fast subset searching in relational databases. The index is based on the creation of group bitmap keys, which are a special case of superimposed codin ...
... collections of sets was also analyzed by Kitagawa, Ishikawa, and Obho [18]. In Ref. [24] a set-based bitmap index is presented, which facilitates the fast subset searching in relational databases. The index is based on the creation of group bitmap keys, which are a special case of superimposed codin ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... against compromised mobile nodes, which often carry the private keys. Integrity validation using redundant information (from different nodes), such as those being used in secure routing, also relies on the trustworthiness of other nodes, which could likewise be a weak link for sophisticated attacks. ...
... against compromised mobile nodes, which often carry the private keys. Integrity validation using redundant information (from different nodes), such as those being used in secure routing, also relies on the trustworthiness of other nodes, which could likewise be a weak link for sophisticated attacks. ...
CD: A Coupled Discretization Algorithm
... values to enable the Naive Bayes classifier to estimate the frequency probabilities [13]; decision tree algorithms cannot handle numeric features in tolerable time directly, and only carry out a selection of nominal attributes [9]; and attribute reduction algorithms in rough set theory can only appl ...
... values to enable the Naive Bayes classifier to estimate the frequency probabilities [13]; decision tree algorithms cannot handle numeric features in tolerable time directly, and only carry out a selection of nominal attributes [9]; and attribute reduction algorithms in rough set theory can only appl ...
K-SVMeans: A Hybrid Clustering Algorithm for Multi
... Traditionally, research in clustering has mainly focused on ”flat” data clustering where the data instances are represented as a vector of homogeneous and uniform set of features, like words in text documents or visual features in image collections. For instance, scientific papers, email messages, b ...
... Traditionally, research in clustering has mainly focused on ”flat” data clustering where the data instances are represented as a vector of homogeneous and uniform set of features, like words in text documents or visual features in image collections. For instance, scientific papers, email messages, b ...
Automated Hierarchical Density Shaving: A Robust Automated
... particular approaches, e.g., biclustering methods for microarray data analysis [33], [3] and directional generative models for text [4]. This rich heritage is largely due to not only the wide applicability of clustering but also the “ill-posed” nature of the problem and the fact that no single metho ...
... particular approaches, e.g., biclustering methods for microarray data analysis [33], [3] and directional generative models for text [4]. This rich heritage is largely due to not only the wide applicability of clustering but also the “ill-posed” nature of the problem and the fact that no single metho ...
Data Mining - Shuigeng Zhou
... Machine learning theory How does learning performance vary with the number of training examples presented What learning algorithms are most appropriate for various types of learning tasks ...
... Machine learning theory How does learning performance vary with the number of training examples presented What learning algorithms are most appropriate for various types of learning tasks ...
Constraint-based Subgraph Extraction through Node Sequencing
... can represent the above three requirements (two user-input constraints and one application-independent min-max principle): 1) the desired number of clusters; 2) the objective function of clustering that reflects the min-max principle, and 3) the upper bound of the similarity between two clusters. In ...
... can represent the above three requirements (two user-input constraints and one application-independent min-max principle): 1) the desired number of clusters; 2) the objective function of clustering that reflects the min-max principle, and 3) the upper bound of the similarity between two clusters. In ...
Effective Pattern Identification Approach for Text Mining
... mining is a variation on a field called data mining that tries taxonomy for classifying sequential pattern mining to find interesting patterns from large databases. In algorithms based on important key features supported by traditional search the users typically look for already known techniques. Re ...
... mining is a variation on a field called data mining that tries taxonomy for classifying sequential pattern mining to find interesting patterns from large databases. In algorithms based on important key features supported by traditional search the users typically look for already known techniques. Re ...
Data Mining - Computer Science Intranet
... Then we count against the full database. If none of the negative border itemsets are frequent, we know that none of the supersets are either. If we find that while ABC was not frequent in the sample, it was frequent in the full database, we expand the border around ABC and check again in a second pa ...
... Then we count against the full database. If none of the negative border itemsets are frequent, we know that none of the supersets are either. If we find that while ABC was not frequent in the sample, it was frequent in the full database, we expand the border around ABC and check again in a second pa ...
LD4KD2014 Linked Data for Knowledge Discovery - CEUR
... such as reliability, heterogeneity, provenance or completeness. Many areas of research have adopted these principles both for the management and dissemination of their own data and for the combined reuse of external data sources. However, the way in which Linked Data can be applicable and beneficial ...
... such as reliability, heterogeneity, provenance or completeness. Many areas of research have adopted these principles both for the management and dissemination of their own data and for the combined reuse of external data sources. However, the way in which Linked Data can be applicable and beneficial ...
Comparative Study of Web Structure Mining Techniques for Links
... The whole process of implementation as described in steps: • Firstly, in proposed method Fuzzy K- Means is used to group the given data set into clusters whereas in previous approach K-Means is used to group the data into clusters. • Secondly, Weighted PageRank is applied on clusters to rerank the d ...
... The whole process of implementation as described in steps: • Firstly, in proposed method Fuzzy K- Means is used to group the given data set into clusters whereas in previous approach K-Means is used to group the data into clusters. • Secondly, Weighted PageRank is applied on clusters to rerank the d ...
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.