IOSR Journal of Computer Engineering (IOSR-JCE)
... A Survey of Fuzzy Based Association Rule Mining to Find Co-Occurrence Relationships this proposed work integrates the fuzzy set concepts in the newly proposed CFP-tree algorithm by constructing a compact sub-tree for a fuzzy frequent item, generating candidates in batch from the compact sub-tree an ...
... A Survey of Fuzzy Based Association Rule Mining to Find Co-Occurrence Relationships this proposed work integrates the fuzzy set concepts in the newly proposed CFP-tree algorithm by constructing a compact sub-tree for a fuzzy frequent item, generating candidates in batch from the compact sub-tree an ...
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)
... although there may be other local minima with lower total sum of distances. The problem of finding the global minimum can only can be solved in general by an exhaustive (or clever, or lucky) choice of starting points, but using several replicates with random starting points typically results in a so ...
... although there may be other local minima with lower total sum of distances. The problem of finding the global minimum can only can be solved in general by an exhaustive (or clever, or lucky) choice of starting points, but using several replicates with random starting points typically results in a so ...
EXTENDING THE ROUGHNESS OF THE DATA VIA
... objects are. The theory of rough sets includes this point of view in the sense that if all the attribute values that define two objects are identical then the similarity between them is one. Many ways for calculating the similarity have been proposed, discussed, analyzed and used. Selecting an appro ...
... objects are. The theory of rough sets includes this point of view in the sense that if all the attribute values that define two objects are identical then the similarity between them is one. Many ways for calculating the similarity have been proposed, discussed, analyzed and used. Selecting an appro ...
Data Mining: Concepts and Techniques
... P(age = “<=30” | buys_computer = “yes”) = 2/9 = 0.222 P(age = “<= 30” | buys_computer = “no”) = 3/5 = 0.6 P(income = “medium” | buys_computer = “yes”) = 4/9 = 0.444 P(income = “medium” | buys_computer = “no”) = 2/5 = 0.4 P(student = “yes” | buys_computer = “yes) = 6/9 = 0.667 P(student ...
... P(age = “<=30” | buys_computer = “yes”) = 2/9 = 0.222 P(age = “<= 30” | buys_computer = “no”) = 3/5 = 0.6 P(income = “medium” | buys_computer = “yes”) = 4/9 = 0.444 P(income = “medium” | buys_computer = “no”) = 2/5 = 0.4 P(student = “yes” | buys_computer = “yes) = 6/9 = 0.667 P(student ...
Analytics in security technical white paper
... being the new form of currency in enterprises, there is a wealth of information that can be gleaned from this data. All it needs is a little bit of time, the right set of skills, and a robust path to follow. A combination of these traits could create the perfect analytics program that may assist an ...
... being the new form of currency in enterprises, there is a wealth of information that can be gleaned from this data. All it needs is a little bit of time, the right set of skills, and a robust path to follow. A combination of these traits could create the perfect analytics program that may assist an ...
ROUGH SETS METHODS IN FEATURE REDUCTION AND
... where µ represents the total data mean and the determinant |Sb | denotes a scalar representation of the between-class scatter matrix, and similarly, the determinant |Sw | denotes a scalar representation of the within-class scatter matrix. Criteria based on minimum concept description. Based on the m ...
... where µ represents the total data mean and the determinant |Sb | denotes a scalar representation of the between-class scatter matrix, and similarly, the determinant |Sw | denotes a scalar representation of the within-class scatter matrix. Criteria based on minimum concept description. Based on the m ...
for Literature Analysis Science Navigation Map: An Interactive Data
... work as feature matrices of papers that are employed to calculate the cosine similarity between papers. For title and abstract, the relational and term-paper matrices, are established with respect to the relation of papers to their terms; for author, author-paper matrix refers to the relation of pap ...
... work as feature matrices of papers that are employed to calculate the cosine similarity between papers. For title and abstract, the relational and term-paper matrices, are established with respect to the relation of papers to their terms; for author, author-paper matrix refers to the relation of pap ...
JaiweiHanDataMining
... Scales linearly: finds a good clustering with a single scan and improves the quality with a few additional scans ...
... Scales linearly: finds a good clustering with a single scan and improves the quality with a few additional scans ...
K-Means Clustering of Shakespeare Sonnets with
... Clustering (SLC) is the task of grouping a set of lines in such a way that lines in the same cluster are more similar to each other than to those in other clusters. K-Means clustering is a very effective clustering technique well known for its observed speed and its simplicity. Its aim is to find th ...
... Clustering (SLC) is the task of grouping a set of lines in such a way that lines in the same cluster are more similar to each other than to those in other clusters. K-Means clustering is a very effective clustering technique well known for its observed speed and its simplicity. Its aim is to find th ...
Lecture 6 - Hui Xiong
... min( Conf(caviar→milk) Conf(caviar→milk), Conf(milk→caviar) ) is also very low ...
... min( Conf(caviar→milk) Conf(caviar→milk), Conf(milk→caviar) ) is also very low ...
IJBH 1/2017
... Mor Peleg is Assoc. Prof at the Dept. of Information Systems, University of Haifa, Israel, since 2003, and has been Department Head in 2009-2012. Her BSc and MSc in Biology and PhD in Information Systems are from the Technion, Israel. She spent 6 years at Stanford BioMedical Research during her post ...
... Mor Peleg is Assoc. Prof at the Dept. of Information Systems, University of Haifa, Israel, since 2003, and has been Department Head in 2009-2012. Her BSc and MSc in Biology and PhD in Information Systems are from the Technion, Israel. She spent 6 years at Stanford BioMedical Research during her post ...
Planning Successful Data Mining Projects
... At the start of your project, review the basic information that is known about your organization’s business situation and strategic issues. These details help identify the business goals to be achieved, key project stakeholders, and solutions currently in place. For example, many companies already h ...
... At the start of your project, review the basic information that is known about your organization’s business situation and strategic issues. These details help identify the business goals to be achieved, key project stakeholders, and solutions currently in place. For example, many companies already h ...
Tutorial: Centrality Measures on Big Graphs: Exact, Approximated
... With the proliferation of huge networks with millions of nodes and billions of edges, the importance of having scalable algorithms for computing centrality indices has become more and more evident, and a number of contributions have been recently proposed, ranging from heuristics that perform extrem ...
... With the proliferation of huge networks with millions of nodes and billions of edges, the importance of having scalable algorithms for computing centrality indices has become more and more evident, and a number of contributions have been recently proposed, ranging from heuristics that perform extrem ...
Concept Decompositions for Large Sparse Text Data using Clustering by Inderjit S. Dhillon and Dharmendra S. Modha
... 1973) between them. Sparsity of the concept vectors is important in that it speaks to the economy or parsimony of the model constituted by them. Also, sparsity is crucial to computational and memory efficiency of the spherical k-means algorithm. In conclusion, the concept vectors produced by the sph ...
... 1973) between them. Sparsity of the concept vectors is important in that it speaks to the economy or parsimony of the model constituted by them. Also, sparsity is crucial to computational and memory efficiency of the spherical k-means algorithm. In conclusion, the concept vectors produced by the sph ...
Maximum Likelihood in Cost-Sensitive Learning: Model
... class proportions. Masnadi-Shirazi and Vasconcelos (2010) describe a cost-sensitive version of the popular support vector machine. Some work has been devoted to the case of example-dependent costs (Zadrozny and Elkan, 2001; Zadrozny et al., 2003). Moreover, some authors have advocated for maximizing ...
... class proportions. Masnadi-Shirazi and Vasconcelos (2010) describe a cost-sensitive version of the popular support vector machine. Some work has been devoted to the case of example-dependent costs (Zadrozny and Elkan, 2001; Zadrozny et al., 2003). Moreover, some authors have advocated for maximizing ...
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.