a two-staged clustering algorithm for multiple scales
... meaning a high intra-class similarity and a low inter-class similarity. The quality of a clustering method is also measured by its ability to discover hidden patterns [1]. There are two kinds of clustering methods -- hierarchical and partitioning. This study used a k-means method (one of the popular ...
... meaning a high intra-class similarity and a low inter-class similarity. The quality of a clustering method is also measured by its ability to discover hidden patterns [1]. There are two kinds of clustering methods -- hierarchical and partitioning. This study used a k-means method (one of the popular ...
Incorporating Data Mining Techniques on Software Cost
... available algorithms developed by Barry Boehm, et al. [9], is a very robust model, it is generate the more accurate result on the basis of past project data that are very similar for our new projects.. However these results were only internally validated, using leave one out cross validation, with t ...
... available algorithms developed by Barry Boehm, et al. [9], is a very robust model, it is generate the more accurate result on the basis of past project data that are very similar for our new projects.. However these results were only internally validated, using leave one out cross validation, with t ...
Aalborg Universitet Segmentation of Nonstationary Time Series with Geometric Clustering
... Our approach to proposing oblique split candidates is agnostic to any specific parametric assumptions on the noise distribution and therefore accommodates without change non-Gaussian or even correlated errors (thus our method is more general than ART, which relies on univariate Gaussian quantiles as ...
... Our approach to proposing oblique split candidates is agnostic to any specific parametric assumptions on the noise distribution and therefore accommodates without change non-Gaussian or even correlated errors (thus our method is more general than ART, which relies on univariate Gaussian quantiles as ...
PPT
... The key principle for effective sampling is the following: – using a sample will work almost as well as using the entire data sets, if the sample is representative – A sample is representative if it has approximately the same property (of interest) as the original set of data ...
... The key principle for effective sampling is the following: – using a sample will work almost as well as using the entire data sets, if the sample is representative – A sample is representative if it has approximately the same property (of interest) as the original set of data ...
Data Mining - Computer Science Intranet
... Our examples have been supermarket baskets. But you don't buy 'bread' you buy a certain brand of bread, with a certain flavour and thickness. eg White Warburton's Toast bread. 2 litre bottle of Tesco's Semi-skimmed milk, not 'milk' We could compact all of the 'milks' and 'breads' together before dat ...
... Our examples have been supermarket baskets. But you don't buy 'bread' you buy a certain brand of bread, with a certain flavour and thickness. eg White Warburton's Toast bread. 2 litre bottle of Tesco's Semi-skimmed milk, not 'milk' We could compact all of the 'milks' and 'breads' together before dat ...
slides in pdf - Università degli Studi di Milano
... Warehouse—tuned for OLAP: complex OLAP queries, multidimensional view, consolidation ...
... Warehouse—tuned for OLAP: complex OLAP queries, multidimensional view, consolidation ...
map reduce approach for computing interesting measure for data cube
... new algorithm called as MapReduce[12] has been proposed in order to efficiently perform cube computation. In this approach, a framework based on MapReduce effectively distributes both data and computation workload. It uses a cube computation algorithm known as MRCube that utilizes techniques to succ ...
... new algorithm called as MapReduce[12] has been proposed in order to efficiently perform cube computation. In this approach, a framework based on MapReduce effectively distributes both data and computation workload. It uses a cube computation algorithm known as MRCube that utilizes techniques to succ ...
clusters
... • Linear search to find the nearest neighbors is not efficient for large training sets. • Indexing structures can be built to speed testing. • For Euclidian distance, a kd-tree can be built that reduces the expected time to find the nearest neighbor to O(log n) in the number of training examples. – ...
... • Linear search to find the nearest neighbors is not efficient for large training sets. • Indexing structures can be built to speed testing. • For Euclidian distance, a kd-tree can be built that reduces the expected time to find the nearest neighbor to O(log n) in the number of training examples. – ...
Databases and Data mining
... • Used to extract records from databases • Original version developed in mid-1970s and called SEQUEL • SQL was introduced as commercial product by Oracle in 1979. • Uses relational algebra to extract data ...
... • Used to extract records from databases • Original version developed in mid-1970s and called SEQUEL • SQL was introduced as commercial product by Oracle in 1979. • Uses relational algebra to extract data ...
CF33497503
... BitMatrix algorithm is described as: 1. Initialize the bitmatrix; 2. L1 = {large 1-itemset}; 3. for (k=2; Lk!=0; k++) do ...
... BitMatrix algorithm is described as: 1. Initialize the bitmatrix; 2. L1 = {large 1-itemset}; 3. for (k=2; Lk!=0; k++) do ...
Linked data and online classifications to organise mined patterns in
... been devoted to the development of efficient and fast algorithms able to deal with huge amounts of data or complex data. Though it has received less attention, the interpretation step can in many cases be as problematic. Indeed, some methods such as association rule mining, frequent itemset search o ...
... been devoted to the development of efficient and fast algorithms able to deal with huge amounts of data or complex data. Though it has received less attention, the interpretation step can in many cases be as problematic. Indeed, some methods such as association rule mining, frequent itemset search o ...
GeneKeyDB: A lightweight, gene-centric, relational database to
... databases and GeneKeyDB can be seen in Table 1. An interesting alternative to the above mentioned databases is BioMART[7]. This is not a database in the conventional sense (though the underlying data can also be downloaded). It extracts and integrates data from several sources, creating customized d ...
... databases and GeneKeyDB can be seen in Table 1. An interesting alternative to the above mentioned databases is BioMART[7]. This is not a database in the conventional sense (though the underlying data can also be downloaded). It extracts and integrates data from several sources, creating customized d ...
Improve Frequent Pattern Mining in Data Stream
... contain the itemset in a batch B and denoted as freq(x). Occurrence frequency is also called as absolute support. Support of X denoted by supp(X) is freq(X) / N, where N is total number of transactions received in W in data stream. It is also called as relative support. Support (A⇒B) = P (A ∪ B). Co ...
... contain the itemset in a batch B and denoted as freq(x). Occurrence frequency is also called as absolute support. Support of X denoted by supp(X) is freq(X) / N, where N is total number of transactions received in W in data stream. It is also called as relative support. Support (A⇒B) = P (A ∪ B). Co ...
01WAIM_camera1 - NDSU Computer Science
... In this section a TC-cube based method for mining non-redundant, low-support, highconfidence rules is introduced. Such rules will be called confident rules. The main interest is in rules with low support, which are important for many application areas such as, natural resource searches, agriculture ...
... In this section a TC-cube based method for mining non-redundant, low-support, highconfidence rules is introduced. Such rules will be called confident rules. The main interest is in rules with low support, which are important for many application areas such as, natural resource searches, agriculture ...
Simplified Swarm Optimization Based Function
... individuals, the understanding of protein–protein interactions (PPI) is the basis to reveal the activity of protein and promotes the study of various diseases and development of new drug. In the past 10 years, substantial work was conducted to promote the research in the field of PPI, such as public ...
... individuals, the understanding of protein–protein interactions (PPI) is the basis to reveal the activity of protein and promotes the study of various diseases and development of new drug. In the past 10 years, substantial work was conducted to promote the research in the field of PPI, such as public ...
Data Mining
... Definitions of data mining (1) • Knowledge discovery in databases is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. (Fayyad, Piatetsky-Shapiro and Smyth, 1996) • An information extraction activity whose goal is to discover hid ...
... Definitions of data mining (1) • Knowledge discovery in databases is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. (Fayyad, Piatetsky-Shapiro and Smyth, 1996) • An information extraction activity whose goal is to discover hid ...
slides in pdf - Università degli Studi di Milano
... Warehouse—tuned for OLAP: complex OLAP queries, multidimensional view, consolidation ...
... Warehouse—tuned for OLAP: complex OLAP queries, multidimensional view, consolidation ...
Classification of Categorical Uncertain Data Using Decision
... of possible values [12]. “Imprecise queries processing” is one well known topic on the value uncertainty. Such a query is associated with a probability that represent the guarantee on its correctness. In co-related uncertainty value of multiple attributes describe by a joint- probability- distributi ...
... of possible values [12]. “Imprecise queries processing” is one well known topic on the value uncertainty. Such a query is associated with a probability that represent the guarantee on its correctness. In co-related uncertainty value of multiple attributes describe by a joint- probability- distributi ...
Entropy-based Subspace Clustering for Mining Numerical Data
... data mining. Clustering is one of the techniques. We consider a database with numerical attributes, in which each transaction is viewed as a multi-dimensional vector. By studying the clusters formed by these vectors, we can discover certain behaviors hidden in the data. Traditional clustering algori ...
... data mining. Clustering is one of the techniques. We consider a database with numerical attributes, in which each transaction is viewed as a multi-dimensional vector. By studying the clusters formed by these vectors, we can discover certain behaviors hidden in the data. Traditional clustering algori ...
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.