Data analytics at work
... master data for further process automation. This was followed by the theoretical background of data mining which was investigated and described based on literature. The promise of data mining is to find the interesting patterns hidden in data. Merely finding patterns is not enough. You must respond ...
... master data for further process automation. This was followed by the theoretical background of data mining which was investigated and described based on literature. The promise of data mining is to find the interesting patterns hidden in data. Merely finding patterns is not enough. You must respond ...
Generating a Diverse Set of High-Quality Clusterings
... information[33,40,42,7]. While these distances are quite popular, they ignore information about the spatial distribution of points within clusters, and so are unable to differentiate between partitions that might be significantly different. Spatially-sensitive distances. In order to rectify this pr ...
... information[33,40,42,7]. While these distances are quite popular, they ignore information about the spatial distribution of points within clusters, and so are unable to differentiate between partitions that might be significantly different. Spatially-sensitive distances. In order to rectify this pr ...
ppt
... For example, in the matching problem we want to “punish” attributes that co-appear Automatic Schema Matching, SDBI, 2006 ...
... For example, in the matching problem we want to “punish” attributes that co-appear Automatic Schema Matching, SDBI, 2006 ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... Transfer learning is intended to transfer knowledge learned from one or more tasks to a new task [1]. In [11] R.K. Ando and T. Zhang introduced the alternating structure optimization (ASO) framework; a learning algorithm is first trained on a set of auxiliary problems. The linear prediction vectors ...
... Transfer learning is intended to transfer knowledge learned from one or more tasks to a new task [1]. In [11] R.K. Ando and T. Zhang introduced the alternating structure optimization (ASO) framework; a learning algorithm is first trained on a set of auxiliary problems. The linear prediction vectors ...
CORDS: Automatic Discovery of Correlations and Soft Functional
... selectivity of conjunctive predicates. The algorithms in [1, 5] do not discover correlation between columns, however: the set of columns over which to build the histogram must be specified a priori. The sash algorithm [14] decomposes the set of columns in a table into disjoint clusters. Columns with ...
... selectivity of conjunctive predicates. The algorithms in [1, 5] do not discover correlation between columns, however: the set of columns over which to build the histogram must be specified a priori. The sash algorithm [14] decomposes the set of columns in a table into disjoint clusters. Columns with ...
Evaluating Multidimensional Visualization Techniques in Data
... Special thanks are due to all members of the Data Mining and Knowledge Management Laboratory for contributing to our seminars with stimulating discussions and ideas. In particular, I thank my colleagues Marketta Hiissa, Piia Hirkman and Minna Kallio for giving me valuable feedback on an earlier vers ...
... Special thanks are due to all members of the Data Mining and Knowledge Management Laboratory for contributing to our seminars with stimulating discussions and ideas. In particular, I thank my colleagues Marketta Hiissa, Piia Hirkman and Minna Kallio for giving me valuable feedback on an earlier vers ...
Introduction to the multivariate analysis
... contact hours with the teacher realized in the form of classes: 60 preparing students for classes: 15 solving problems and preparing reports: 45 ...
... contact hours with the teacher realized in the form of classes: 60 preparing students for classes: 15 solving problems and preparing reports: 45 ...
Clustering Methods for Microarray Gene Expression Data
... be represented as C {C1, . . . ,Cm} where Cj’s are disjoint clusters. Sample-based clustering can be used to reveal sample types, which are possibly indistinguishable by traditional morphology-based approaches (Jiang et al., 2004). Traditional clustering techniques can be classified into two main ...
... be represented as C {C1, . . . ,Cm} where Cj’s are disjoint clusters. Sample-based clustering can be used to reveal sample types, which are possibly indistinguishable by traditional morphology-based approaches (Jiang et al., 2004). Traditional clustering techniques can be classified into two main ...
Managing and Mining Graph Data
... Prediction rules of kernel methods. (a) An example of labeled graphs. Vertices and edges are labeled by uppercase and lowercase letters, respectively. By traversing along the bold edges, the label sequence (2.1) is produced. (b) By repeating random walks, one can construct a list of probabilities. A ...
... Prediction rules of kernel methods. (a) An example of labeled graphs. Vertices and edges are labeled by uppercase and lowercase letters, respectively. By traversing along the bold edges, the label sequence (2.1) is produced. (b) By repeating random walks, one can construct a list of probabilities. A ...
Mining Sequential Alarm Patterns in a Telecommunication Database
... a transaction-time associated with each transaction. A sequential pattern also consists of a list of sets of items. The problem is to find all sequential patterns with a userspecified minimum support, where the support of a sequential pattern is the percentage of data-sequences that contain the patt ...
... a transaction-time associated with each transaction. A sequential pattern also consists of a list of sets of items. The problem is to find all sequential patterns with a userspecified minimum support, where the support of a sequential pattern is the percentage of data-sequences that contain the patt ...
Time Series Knowledge Mining
... in databases and data mining ”are often used interchangeably” but also adopts the view of Fayyad et al. and defines KDD as ”the process of finding useful information and patterns in data” while data mining is a sub step in the process. Ultsch defines data mining as the inspection of data with the ai ...
... in databases and data mining ”are often used interchangeably” but also adopts the view of Fayyad et al. and defines KDD as ”the process of finding useful information and patterns in data” while data mining is a sub step in the process. Ultsch defines data mining as the inspection of data with the ai ...
Chapter 8.3 - UCLA Computer Science
... 8.3.1 Sequential Pattern Mining: Concepts and Primitives “What is sequential pattern mining?” Sequential pattern mining is the mining of frequently occurring ordered events or subsequences as patterns. An example of a sequential pattern is “Customers who buy a Canon digital camera are likely to buy ...
... 8.3.1 Sequential Pattern Mining: Concepts and Primitives “What is sequential pattern mining?” Sequential pattern mining is the mining of frequently occurring ordered events or subsequences as patterns. An example of a sequential pattern is “Customers who buy a Canon digital camera are likely to buy ...
yes - Computer Science, Stony Brook University
... Tree STARTS as a single node representing all training dataset (samples) IF the samples are ALL in the same class, THEN the node becomes a LEAF and is labeled with that class (or we may apply majority voting or other method to decide the class on the leaf) OTHERWISE, the algorithm uses an entropy-ba ...
... Tree STARTS as a single node representing all training dataset (samples) IF the samples are ALL in the same class, THEN the node becomes a LEAF and is labeled with that class (or we may apply majority voting or other method to decide the class on the leaf) OTHERWISE, the algorithm uses an entropy-ba ...
w - Mining of Massive Datasets
... y … class ({+1, -1}, or a real number) J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org ...
... y … class ({+1, -1}, or a real number) J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org ...
ppt
... – A bank wants to classify its customers based on whether they are expected to pay back their approved loans – The history of past customers is used to train the classifier ...
... – A bank wants to classify its customers based on whether they are expected to pay back their approved loans – The history of past customers is used to train the classifier ...
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.