A Parallel Clustering Method Combined Information Bottleneck
... The evaluation of unsupervised clustering result is a difficult problem. Visualization is a good mean to improve it. However, in practical, many problems’ feature variable vectors are in high dimensions. Feature extraction can decrease the dimension of input efficiently. Many feature extraction meth ...
... The evaluation of unsupervised clustering result is a difficult problem. Visualization is a good mean to improve it. However, in practical, many problems’ feature variable vectors are in high dimensions. Feature extraction can decrease the dimension of input efficiently. Many feature extraction meth ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... for proper Dirichlet process mixture models. The SUGS algorithm was mutated within a variational Bayes framework which was used to collect different approximations of the posterior distribution. It provided a probability distribution to allocate data to a cluster [3]. Luis Talavera et al explored op ...
... for proper Dirichlet process mixture models. The SUGS algorithm was mutated within a variational Bayes framework which was used to collect different approximations of the posterior distribution. It provided a probability distribution to allocate data to a cluster [3]. Luis Talavera et al explored op ...
Using Association Rules to Make Rule
... rule targets are constrained by the class labels, association rules become class (or constraint) association rules and they can be used for classification purpose. All association rule mining algorithms, e.g. Apriori (Agrawal & Srikant 1994) and FP-growth (Han, Pei & Yin 2000), can be easily adapted ...
... rule targets are constrained by the class labels, association rules become class (or constraint) association rules and they can be used for classification purpose. All association rule mining algorithms, e.g. Apriori (Agrawal & Srikant 1994) and FP-growth (Han, Pei & Yin 2000), can be easily adapted ...
Detecting Blackhole and Volcano Patterns in Directed
... directed graph, a blackhole pattern is a group which is made of a set of nodes in a way such that there are only inlinks to this group from the rest nodes in the graph. In contrast, a volcano pattern is a group which only has outlinks to the rest nodes in the graph. To the best of our knowledge, thi ...
... directed graph, a blackhole pattern is a group which is made of a set of nodes in a way such that there are only inlinks to this group from the rest nodes in the graph. In contrast, a volcano pattern is a group which only has outlinks to the rest nodes in the graph. To the best of our knowledge, thi ...
Detection and Visualization of Subspace Cluster Hierarchies
... An example of such a hierarchy is depicted in Figure 1 (left). Two one-dimensional (1D) cluster (C and D) are embedded within one two-dimensional (2D) cluster (B). In addition, cluster C is embedded within both 2D clusters A and B. Detecting such relationships of subspace clusters is obviously a hie ...
... An example of such a hierarchy is depicted in Figure 1 (left). Two one-dimensional (1D) cluster (C and D) are embedded within one two-dimensional (2D) cluster (B). In addition, cluster C is embedded within both 2D clusters A and B. Detecting such relationships of subspace clusters is obviously a hie ...
Co-occurrence Patterns in Market-Basket Data
... is in a DBMS for reasons that go well beyond the analysis capabilities of the DBMS, even if these are often inadequate. And if the past is any indication, the DB vendors will try to expand SQL to support whatever DM capabilities the market will pay for—and it’s not clear that this is the right arc ...
... is in a DBMS for reasons that go well beyond the analysis capabilities of the DBMS, even if these are often inadequate. And if the past is any indication, the DB vendors will try to expand SQL to support whatever DM capabilities the market will pay for—and it’s not clear that this is the right arc ...
A review of feature selection methods with applications
... place for two main reasons: 1) reduction of the size of the dataset in order to achieve more efficient analysis, and 2) adaptation of the dataset to best suit the selected analysis method. The former reason is more important nowadays because of the plethora of developed analysis methods that are at ...
... place for two main reasons: 1) reduction of the size of the dataset in order to achieve more efficient analysis, and 2) adaptation of the dataset to best suit the selected analysis method. The former reason is more important nowadays because of the plethora of developed analysis methods that are at ...
Steven Carnovale, Ph.D. - Reverse Logistics and Sustainability
... • Finds relationships in data that may not be readily apparent with descriptive analysis • Transition from simply examining extant data to making predictions with/from it • Leverages statistics/probability theory to ascertain individual likelihoods ...
... • Finds relationships in data that may not be readily apparent with descriptive analysis • Transition from simply examining extant data to making predictions with/from it • Leverages statistics/probability theory to ascertain individual likelihoods ...
Oracle Data Mining Programmer`s Guide
... Oracle Corporation; they are provided under a license agreement containing restrictions on use and disclosure and are also protected by copyright, patent and other intellectual and industrial property laws. Reverse engineering, disassembly or decompilation of the Programs, except to the extent requi ...
... Oracle Corporation; they are provided under a license agreement containing restrictions on use and disclosure and are also protected by copyright, patent and other intellectual and industrial property laws. Reverse engineering, disassembly or decompilation of the Programs, except to the extent requi ...
Data Miing / Web Data Mining
... Subtree Replacement: merge a subtree into a leaf node Using a set of data different from the training data At a tree node, if the accuracy without splitting is higher than the accuracy with splitting, replace the subtree with a leaf node; label it using the majority class color red ...
... Subtree Replacement: merge a subtree into a leaf node Using a set of data different from the training data At a tree node, if the accuracy without splitting is higher than the accuracy with splitting, replace the subtree with a leaf node; label it using the majority class color red ...
Data streams - Videolectures
... Source: Linear Road: A Stream Data Management Benchmark, VLDB 2004 MMDSS’07 – G.Hébrail – Data stream management and mining – Slide 13 ...
... Source: Linear Road: A Stream Data Management Benchmark, VLDB 2004 MMDSS’07 – G.Hébrail – Data stream management and mining – Slide 13 ...
DATA CLUSTERING: FROM DOCUMENTS TO THE WEB
... and eliminate their negative effects), statistical distribution, cluster shape, cluster size, cluster density, cluster separation (an algorithm must be able to detect overlapping clusters). The particular attention is paid to the problem of high dimensional data. Clustering algorithms based on proxi ...
... and eliminate their negative effects), statistical distribution, cluster shape, cluster size, cluster density, cluster separation (an algorithm must be able to detect overlapping clusters). The particular attention is paid to the problem of high dimensional data. Clustering algorithms based on proxi ...
Gain ratio based fuzzy weighted association rule mining classifier for
... part is the conjunction of features selected from the features describing the training instances. Dimension of search space has an exponential relation to the number of features and values considered. In Shaik & Yeasin (2009) proposed ASI [Adaptive Subspace Iteration] adapts one to many mappings bet ...
... part is the conjunction of features selected from the features describing the training instances. Dimension of search space has an exponential relation to the number of features and values considered. In Shaik & Yeasin (2009) proposed ASI [Adaptive Subspace Iteration] adapts one to many mappings bet ...
A clustering-based visualization of spatial patterns
... focus on transforming spatial data into transactional data where classical itemset mining algorithms could be used [14, 4]. In [14], authors presented an efficient method for mining association rules in geographic information databases. This method enumerates neighbors to ”materialize” a set of tran ...
... focus on transforming spatial data into transactional data where classical itemset mining algorithms could be used [14, 4]. In [14], authors presented an efficient method for mining association rules in geographic information databases. This method enumerates neighbors to ”materialize” a set of tran ...
dm_clustering1
... We convert the credit rating values ‘very good’, ‘good, ‘medium’, ‘poor’, and ‘very poor’ to: 0:4 using a function ; And use the Jaccard distance function for the services: dservices(ser1,ser2)= 1- (|ser1ser2|)/(|ser1 ser2|) Putting this together distance between two customers u and v can be comp ...
... We convert the credit rating values ‘very good’, ‘good, ‘medium’, ‘poor’, and ‘very poor’ to: 0:4 using a function ; And use the Jaccard distance function for the services: dservices(ser1,ser2)= 1- (|ser1ser2|)/(|ser1 ser2|) Putting this together distance between two customers u and v can be comp ...
Big Data`s Disparate Impact
... Advocates of algorithmic techniques like data mining argue that these techniques eliminate human biases from the decision-making process. But an algorithm is only as good as the data it works with. Data is frequently imperfect in ways that allow these algorithms to inherit the prejudices of prior de ...
... Advocates of algorithmic techniques like data mining argue that these techniques eliminate human biases from the decision-making process. But an algorithm is only as good as the data it works with. Data is frequently imperfect in ways that allow these algorithms to inherit the prejudices of prior de ...
Advances in Document Clustering with Evolutionary
... which it is applied to two-dimensional data or multidimensional data as in the case of the text documents. Hence, this section is divided into two subsections. The first subsection is devoted to the studies that dealt with the conventional algorithms for document clustering (the dash-dotted line in ...
... which it is applied to two-dimensional data or multidimensional data as in the case of the text documents. Hence, this section is divided into two subsections. The first subsection is devoted to the studies that dealt with the conventional algorithms for document clustering (the dash-dotted line in ...
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.