Deliverable D3.1 Research Report on TDM Landscape
... unknown information, by automatically extracting and relating information from different (…) resources, to reveal otherwise hidden meanings” (Hearst, 1999), in other words, “an exploratory data analysis that leads to the discovery of heretofore unknown information, or to answers for ques ...
... unknown information, by automatically extracting and relating information from different (…) resources, to reveal otherwise hidden meanings” (Hearst, 1999), in other words, “an exploratory data analysis that leads to the discovery of heretofore unknown information, or to answers for ques ...
Efficiently Mining Asynchronous Periodic Patterns
... efficient algorithm E-MAP. Our propose algorithm finds all maximal complex patterns in a single step algorithm using a single dataset scan without mining single event and multiple events patterns explicitly, while asynchronous periodic patterns are mined using the same depth first search enumeration ...
... efficient algorithm E-MAP. Our propose algorithm finds all maximal complex patterns in a single step algorithm using a single dataset scan without mining single event and multiple events patterns explicitly, while asynchronous periodic patterns are mined using the same depth first search enumeration ...
10ClusBasic
... 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 ...
Clustering
... 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 ...
Course : Data mining Topic : Locality
... searching by hashing should be able to locate similar objects locality-sensitive hashing collision probability for similar objects is high enough collision probability of dissimilar objects is low randomized data structure guarantees (running time and quality) hold in expectation (with high probabi ...
... searching by hashing should be able to locate similar objects locality-sensitive hashing collision probability for similar objects is high enough collision probability of dissimilar objects is low randomized data structure guarantees (running time and quality) hold in expectation (with high probabi ...
Towards Data Mining Services on the Internet with a Multiple
... decision-making process. The high cost of data mining software can be prohibitive for small to medium range organisations. In such cases, the application service providers are a viable and intuitive solution. Need for immediate benefits. The benefits gained by implementing data mining infrastructure ...
... decision-making process. The high cost of data mining software can be prohibitive for small to medium range organisations. In such cases, the application service providers are a viable and intuitive solution. Need for immediate benefits. The benefits gained by implementing data mining infrastructure ...
The Molecular Feature Miner - MolFea - Beilstein
... We will start with a simple minimum frequency constraint freq(f, D) ≥ t. This constraint has the important property of anti monotonicity. To illustrate anti-monotonicity let us assume, we have two fragments g and s and we know that: - g is more general than s (i.e. g ≤ s; e.g. g: C-O, s: C-O-S), and ...
... We will start with a simple minimum frequency constraint freq(f, D) ≥ t. This constraint has the important property of anti monotonicity. To illustrate anti-monotonicity let us assume, we have two fragments g and s and we know that: - g is more general than s (i.e. g ≤ s; e.g. g: C-O, s: C-O-S), and ...
Knowledge Discovery with Genetic Programming for Providing
... not interesting rules due to the fact that interest is a more ambitious and difficult objective. There are two methods to select interesting rules, namely subjective and objective methods (Liu et al., 2000). The first ones are domain dependent and user directed. On the other hand the objective metho ...
... not interesting rules due to the fact that interest is a more ambitious and difficult objective. There are two methods to select interesting rules, namely subjective and objective methods (Liu et al., 2000). The first ones are domain dependent and user directed. On the other hand the objective metho ...
Statistical Machine Learning for Data Mining and
... (BMAL). In contrast to traditional approaches, the BMAL method searches a batch of informative examples for labeling. To develop an effective algorithm, the BMAL task is formulated into a convex optimization problem and a novel bound optimization algorithm is proposed to efficiently solve it with globa ...
... (BMAL). In contrast to traditional approaches, the BMAL method searches a batch of informative examples for labeling. To develop an effective algorithm, the BMAL task is formulated into a convex optimization problem and a novel bound optimization algorithm is proposed to efficiently solve it with globa ...
Information-Theoretic Tools for Mining Database Structure from
... In our approach, rather than viewing the data as being inconsistent or incomplete with respect to a given schema, we consider the schema to be potentially inconsistent or incomplete with respect to a given data instance. Our contributions are the following. • We propose a set of information-theoreti ...
... In our approach, rather than viewing the data as being inconsistent or incomplete with respect to a given schema, we consider the schema to be potentially inconsistent or incomplete with respect to a given data instance. Our contributions are the following. • We propose a set of information-theoreti ...
6 slides per page - DataBase and Data Mining Group
... Decision boundary is distorted by noise point ...
... Decision boundary is distorted by noise point ...
Classification: basic concepts
... Suppose the attribute income partitions D into 10 in D1: {low, medium} and 4 in D2 giniincome{low,medium} ( D) 10 Gini( D1 ) 4 Gini( D2 ) ...
... Suppose the attribute income partitions D into 10 in D1: {low, medium} and 4 in D2 giniincome{low,medium} ( D) 10 Gini( D1 ) 4 Gini( D2 ) ...
Information-Theoretic Tools for Mining Database Structure from
... some of the data redundancy that would occur naturally when associating different types of information together. However, it is not obvious how to apply normalization or how to define the information content of a database in an environment where the given schema and constraints may be incorrect or ...
... some of the data redundancy that would occur naturally when associating different types of information together. However, it is not obvious how to apply normalization or how to define the information content of a database in an environment where the given schema and constraints may be incorrect or ...
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.