1.Non-trivial extraction of ______, previously unknown and
... 1. The purpose of preprocessing is to transform the raw input data into an appropriate format for subsequent analysis 2.Data mining tasks are generally divided into two major categories 3.The attribute to be predicted is commonly known as the target or dependent variable 4.Association analysis is us ...
... 1. The purpose of preprocessing is to transform the raw input data into an appropriate format for subsequent analysis 2.Data mining tasks are generally divided into two major categories 3.The attribute to be predicted is commonly known as the target or dependent variable 4.Association analysis is us ...
Unit-1 - WordPress.com
... b) What is web usage mining? Explain with suitable example. [2008] 3) A heterogeneous database system consists of multiple database systems that are defined independently, but that need to exchange transform information among themselves and answer global queries. Discuss how to process a descriptive ...
... b) What is web usage mining? Explain with suitable example. [2008] 3) A heterogeneous database system consists of multiple database systems that are defined independently, but that need to exchange transform information among themselves and answer global queries. Discuss how to process a descriptive ...
Multiresolution Vector Quantized approximation (MVQ)
... datasets on secondary memory Algorithm has two phases: Phase 1: a candidate selection phase given a threshold r , finds a set of all discords at distance at least r from their nearest neighbor Phase 2: a discord refinement phase remove all false discords from the candidate set ...
... datasets on secondary memory Algorithm has two phases: Phase 1: a candidate selection phase given a threshold r , finds a set of all discords at distance at least r from their nearest neighbor Phase 2: a discord refinement phase remove all false discords from the candidate set ...
Understanding Users` Interaction Behavior with an - CEUR
... ultimate goal in an adaptive educational game such as PC is to help a higher number of students learn the desired skills through interacting with the game. Achieving such an objective requires a pedagogical agent which maintains an accurate understanding of individual differences among users and pro ...
... ultimate goal in an adaptive educational game such as PC is to help a higher number of students learn the desired skills through interacting with the game. Achieving such an objective requires a pedagogical agent which maintains an accurate understanding of individual differences among users and pro ...
Novel Intrusion Detection System Using Hybrid Approach
... a back propagation neural network based approach, proposed technique performs better in terms of false positive rate, computational time and cost. In paper [10], authors used classification model for misuse and anomaly attack detection using decision tree algorithm. C5, C4.5 and ID3 algorithms are u ...
... a back propagation neural network based approach, proposed technique performs better in terms of false positive rate, computational time and cost. In paper [10], authors used classification model for misuse and anomaly attack detection using decision tree algorithm. C5, C4.5 and ID3 algorithms are u ...
spatio-temporal structures characterization based on multi
... large amount of information contained in SITS, the quantity of information recquired to represent data is a crucial point. This paper addresses the problem of representing objectively and shortly the information contained in SITS by unsupervised clustering. From a compression point of view, clusteri ...
... large amount of information contained in SITS, the quantity of information recquired to represent data is a crucial point. This paper addresses the problem of representing objectively and shortly the information contained in SITS by unsupervised clustering. From a compression point of view, clusteri ...
AN EFFICIENT HILBERT CURVE
... such that the data points within a cluster are more similar to each other than data points in di erent clusters. Cluster analysis has been widely applied to many areas such as medicine, social studies, bioinformatics, map regions and GIS, etc. In recent years, many researchers have focused on nding ...
... such that the data points within a cluster are more similar to each other than data points in di erent clusters. Cluster analysis has been widely applied to many areas such as medicine, social studies, bioinformatics, map regions and GIS, etc. In recent years, many researchers have focused on nding ...
Data Warehousing-Cubing Algorithms
... the cubes which have only those cuboids which have at least a minimum of 'k' support, where 'k' is a threshold. All cuboids of support less than 'k' are pruned, thereby reducing the size of data cube. This process is done to reduce the size of data cube without losing out on much of the information. ...
... the cubes which have only those cuboids which have at least a minimum of 'k' support, where 'k' is a threshold. All cuboids of support less than 'k' are pruned, thereby reducing the size of data cube. This process is done to reduce the size of data cube without losing out on much of the information. ...
2170715 - Gujarat Technological University
... BI and DW architectures and its types - Relation between BI and DW - OLAP (Online analytical processing) definitions - Difference between OLAP and OLTP - Dimensional analysis - What are cubes? Drill-down and roll-up - slice and dice or rotation - OLAP models - ROLAP versus MOLAP - defining schemas: ...
... BI and DW architectures and its types - Relation between BI and DW - OLAP (Online analytical processing) definitions - Difference between OLAP and OLTP - Dimensional analysis - What are cubes? Drill-down and roll-up - slice and dice or rotation - OLAP models - ROLAP versus MOLAP - defining schemas: ...
Integrative data mining for genomics and proteomics
... performing large-scale studies to collectively analyze many different data sets. This approach represents a paradigm shift away from traditional single-gene biology, and often involves statistical analyses focusing on the occurrence of particular features (e.g. folds, functions, interactions, pseudo ...
... performing large-scale studies to collectively analyze many different data sets. This approach represents a paradigm shift away from traditional single-gene biology, and often involves statistical analyses focusing on the occurrence of particular features (e.g. folds, functions, interactions, pseudo ...
An FP-Growth Approach to Mining Association Rules
... association rule is an consequence in the form of X→Y, where X, Y ⊂ I are sets of items called item sets, and X ∩ Y = Ø. X is called originator while Y is called resultant, the rule means X implies Y. There are two essential basic measures for association rules, support (s) and confidence (c). Since ...
... association rule is an consequence in the form of X→Y, where X, Y ⊂ I are sets of items called item sets, and X ∩ Y = Ø. X is called originator while Y is called resultant, the rule means X implies Y. There are two essential basic measures for association rules, support (s) and confidence (c). Since ...
a review on various text mining techniques and algorithms
... Association rule mining (ARM) [3] is a technique used to discover relationships among a large set of variables in a data set. It has been applied to a variety of industry settings and disciplines but has, to date, not been widely used in the social sciences, specifically in education, counseling, an ...
... Association rule mining (ARM) [3] is a technique used to discover relationships among a large set of variables in a data set. It has been applied to a variety of industry settings and disciplines but has, to date, not been widely used in the social sciences, specifically in education, counseling, an ...
Background - BrainMass
... As you know, Retro is interested in offering different trim packages for its models (similar to Honda's DX, LX, and EX model trim levels). Currently, customers are allowed to choose individual options and accessories when they place a custom order. Enrique Munoz, the manufacturing manager, thinks th ...
... As you know, Retro is interested in offering different trim packages for its models (similar to Honda's DX, LX, and EX model trim levels). Currently, customers are allowed to choose individual options and accessories when they place a custom order. Enrique Munoz, the manufacturing manager, thinks th ...
Fast Rank-2 Nonnegative Matrix Factorization for
... descent framework is applied to rank-2 NMF, each subproblem requires a solution for nonnegative least squares (NNLS) with only two columns. We design the algorithm for rank2 NMF by exploiting the fact that an exhaustive search for the optimal active set can be performed extremely fast when solving t ...
... descent framework is applied to rank-2 NMF, each subproblem requires a solution for nonnegative least squares (NNLS) with only two columns. We design the algorithm for rank2 NMF by exploiting the fact that an exhaustive search for the optimal active set can be performed extremely fast when solving t ...
Cluster analysis
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It will often be necessary to modify data preprocessing and model parameters until the result achieves the desired properties.Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek βότρυς ""grape"") and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals.Cluster analysis was originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Zubin in 1938 and Robert Tryon in 1939 and famously used by Cattell beginning in 1943 for trait theory classification in personality psychology.