Learning Classifiers from Only Positive and Unlabeled Data
... The scenario here is that the training data are drawn randomly from p(x, y, s), but for each tuple hx, y, si that is drawn, only hx, si is recorded. The scenario of [21] is that two training sets are drawn independently from p(x, y, s). From the first set all x such that s = 1 are recorded; these ar ...
... The scenario here is that the training data are drawn randomly from p(x, y, s), but for each tuple hx, y, si that is drawn, only hx, si is recorded. The scenario of [21] is that two training sets are drawn independently from p(x, y, s). From the first set all x such that s = 1 are recorded; these ar ...
Simultaneously Discovering Attribute Matching and Cluster
... Another challenging task given multiple data sources is to carry out meaningful meta-analysis that combines results of several studies on different datasets to address a set of related research hypotheses. Finding correspondences among distinct patterns that are observed in different scientific dataset ...
... Another challenging task given multiple data sources is to carry out meaningful meta-analysis that combines results of several studies on different datasets to address a set of related research hypotheses. Finding correspondences among distinct patterns that are observed in different scientific dataset ...
Data integration, pathway analysis and mining for systems
... he provided to me at work. I thank my supervisor, Professor Kimmo Kaski, Head of the Centre of Excellence, Department of Biomedical Engineering and Computational Science (BECS) of Helsinki University of Technology (TKK; called Aalto University School of Science and Technology since January 2010), fo ...
... he provided to me at work. I thank my supervisor, Professor Kimmo Kaski, Head of the Centre of Excellence, Department of Biomedical Engineering and Computational Science (BECS) of Helsinki University of Technology (TKK; called Aalto University School of Science and Technology since January 2010), fo ...
Rough Sets in KDD A Tutorial
... Boolean Reasoning (RSBR). Attribute selection based RS with Heuristics (RSH). Rule discovery by GDT-RS. ...
... Boolean Reasoning (RSBR). Attribute selection based RS with Heuristics (RSH). Rule discovery by GDT-RS. ...
Time Series Contextual Anomaly Detection for Detecting Stock
... In this thesis, we focus on contextual/local anomaly detection within a group of similar time series. The context is defined both in terms of similarity to the neighbourhood data points of each time series and similarity of time series pattern with respect to the rest of time series in the group. Lo ...
... In this thesis, we focus on contextual/local anomaly detection within a group of similar time series. The context is defined both in terms of similarity to the neighbourhood data points of each time series and similarity of time series pattern with respect to the rest of time series in the group. Lo ...
Building Association-Rule Based Sequential Classifiers for Web
... mining and computer networks. In the data mining area, most algorithms are designed to deal with a database consisting of a collection of records (see Quinlan, 1993; Breiman et al., 1984 for example). These records store the transaction data in applications such as supermarkets. The focus of researc ...
... mining and computer networks. In the data mining area, most algorithms are designed to deal with a database consisting of a collection of records (see Quinlan, 1993; Breiman et al., 1984 for example). These records store the transaction data in applications such as supermarkets. The focus of researc ...
Efficient Feature Detection for Sequence Classification in a
... family or not. These sequences can thus be divided into two disjoint classes: olfactory and noolfactory, and from these classes we can extract sequential patterns to be used as attributes in a classification algorithm (as is being proposed in [9]). The question we try to answer in this paper is: whi ...
... family or not. These sequences can thus be divided into two disjoint classes: olfactory and noolfactory, and from these classes we can extract sequential patterns to be used as attributes in a classification algorithm (as is being proposed in [9]). The question we try to answer in this paper is: whi ...
Analyzing the solutions of DEA through information visualization and
... Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guideli ...
... Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guideli ...
Cluster ensembles
... accurate results on average as the ensemble approach takes into account the biases of individual solutions.8,9 2. Robust clustering. It is well known that the popular clustering algorithms often fail spectacularly for certain datasets that do not match well with the modeling assumptions.10 A cluster ...
... accurate results on average as the ensemble approach takes into account the biases of individual solutions.8,9 2. Robust clustering. It is well known that the popular clustering algorithms often fail spectacularly for certain datasets that do not match well with the modeling assumptions.10 A cluster ...
Resilient Distributed Datasets - www
... Although an interface based on coarse-grained transformations may at first seem limited, RDDs are a good fit for many parallel applications, because these applications naturally apply the same operation to multiple data items. Indeed, we show that RDDs can efficiently express many cluster programmin ...
... Although an interface based on coarse-grained transformations may at first seem limited, RDDs are a good fit for many parallel applications, because these applications naturally apply the same operation to multiple data items. Indeed, we show that RDDs can efficiently express many cluster programmin ...
Comparison of Chi-Square Based Algorithms for Discretization of
... The supervised algorithms use priori known class labels information while unsupervised methods do not use such kind of information. In the static discretization algorithms, number of intervals is determined for each variable independently. Contrarily, the dynamic algorithms determine a possible numb ...
... The supervised algorithms use priori known class labels information while unsupervised methods do not use such kind of information. In the static discretization algorithms, number of intervals is determined for each variable independently. Contrarily, the dynamic algorithms determine a possible numb ...
Visually Mining Through Cluster Hierarchies
... introduced to decide whether a local maximum is significant: The first parameter specifies the minimum cluster size, i.e. how many objects must be located between two significant local maxima. The second parameter specifies the ratio between the reachability of a significant local maximum m and the ...
... introduced to decide whether a local maximum is significant: The first parameter specifies the minimum cluster size, i.e. how many objects must be located between two significant local maxima. The second parameter specifies the ratio between the reachability of a significant local maximum m and the ...
A Perspective on Data Mining - Center for Data Insight
... processes, their distribution processes, and their marketing processes. This historical information can be “mined” to develop predictive models to guide future decisionmaking. • The field of machine learning has continued to evolve in the academic communities. New concepts, new algorithms and new co ...
... processes, their distribution processes, and their marketing processes. This historical information can be “mined” to develop predictive models to guide future decisionmaking. • The field of machine learning has continued to evolve in the academic communities. New concepts, new algorithms and new co ...
Pattern Based Sequence Classification*
... leveraged existing sequence mining techniques to efficiently select features from a sequence dataset. The experimental results showed that BayesFM (combination of Naı̈ve Bayes and FeatureMine) is better than Naı̈ve Bayes only. Although a pruning method is used in their algorithm, there was still a l ...
... leveraged existing sequence mining techniques to efficiently select features from a sequence dataset. The experimental results showed that BayesFM (combination of Naı̈ve Bayes and FeatureMine) is better than Naı̈ve Bayes only. Although a pruning method is used in their algorithm, there was still a l ...
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.