One Click Mining - Polo Club of Data Science
... ration/exploitation problem where the payoffs correspond to the utility of the mined patterns. Since this utility is measured by an evolving approximation of the user interest, we address this problem by a bandit algorithm suitable for shifting payoffs [Cesa-Bianchi and Lugosi, 2006]. Overall, we en ...
... ration/exploitation problem where the payoffs correspond to the utility of the mined patterns. Since this utility is measured by an evolving approximation of the user interest, we address this problem by a bandit algorithm suitable for shifting payoffs [Cesa-Bianchi and Lugosi, 2006]. Overall, we en ...
Visual Reconciliation of Alternative Similarity Spaces
... Different clustering methods have been proposed for dealing with alternative similarity spaces. Pfitzner et al. proposed a theoretical framework for evaluating the quality of clusterings through pairwise estimation of similarity [27]. The area of multi-view clustering [4] analyzes cases when data ca ...
... Different clustering methods have been proposed for dealing with alternative similarity spaces. Pfitzner et al. proposed a theoretical framework for evaluating the quality of clusterings through pairwise estimation of similarity [27]. The area of multi-view clustering [4] analyzes cases when data ca ...
DDT: Design and Evaluation of a Dynamic Program Analysis
... structure selection is also a problem in legacy code. For example, if a developer created a custom map that fit well into processor cache lines in 2002, that map would likely have suboptimal performance using the caches in modern processors. Choosing data structures is very difficult, and poor data ...
... structure selection is also a problem in legacy code. For example, if a developer created a custom map that fit well into processor cache lines in 2002, that map would likely have suboptimal performance using the caches in modern processors. Choosing data structures is very difficult, and poor data ...
1435596563
... Markov models is a reasonable choice as they are compact, simple and based on well-established theory. Several Markov models were proposed for modelling user Web data: first-order Markov model, hybrid-order tree-like Markov model [10], prediction by partial match forest [7], kthorder Markov models [ ...
... Markov models is a reasonable choice as they are compact, simple and based on well-established theory. Several Markov models were proposed for modelling user Web data: first-order Markov model, hybrid-order tree-like Markov model [10], prediction by partial match forest [7], kthorder Markov models [ ...
Discovering Interesting Exception Rules with Rule Pair
... Under appropriate assumptions, our exception/deviation structures can be classified into the eleven structures which are shown in Figure 2. Association rule discovery [2] assumes a transaction data set, which has only binary attributes. Each attribute can take either “y” or “n” as its value, and mos ...
... Under appropriate assumptions, our exception/deviation structures can be classified into the eleven structures which are shown in Figure 2. Association rule discovery [2] assumes a transaction data set, which has only binary attributes. Each attribute can take either “y” or “n” as its value, and mos ...
An approach to improve the efficiency of apriori algorithm
... Large number of candidate and frequent item sets are to be handled and results in increased cost and waste of time. Example: if number of frequent (k-1) items is 104 then almost 107 Ck need to be generated and tested [2]. So scanning of a database is done many times to find Ck ...
... Large number of candidate and frequent item sets are to be handled and results in increased cost and waste of time. Example: if number of frequent (k-1) items is 104 then almost 107 Ck need to be generated and tested [2]. So scanning of a database is done many times to find Ck ...
the Stream Mill Experience
... it supports a very powerful query language), the task remains a formidable one. To the best of our knowledge on-line data stream mining has not been attempted previously by other DSMS projects. Providing a full suite of well-integrated on-line mining functions represents only the first of these chal ...
... it supports a very powerful query language), the task remains a formidable one. To the best of our knowledge on-line data stream mining has not been attempted previously by other DSMS projects. Providing a full suite of well-integrated on-line mining functions represents only the first of these chal ...
this PDF file - SEER-UFMG
... Focusing. In real situations, textual data are collected and stored, usually including almost all kinds of information about the problem domain. However, many applications are usually related to only a few aspects of the problem domain. Therefore, it is naturally more efficient to select and focus o ...
... Focusing. In real situations, textual data are collected and stored, usually including almost all kinds of information about the problem domain. However, many applications are usually related to only a few aspects of the problem domain. Therefore, it is naturally more efficient to select and focus o ...
Learning Approximate Sequential Patterns for Classification
... In this paper, we present an automated approach to discover patterns that can distinguish between sequences belonging to different labeled groups. Our method searches for approximately conserved motifs that occur with varying statistical properties in positive and negative training examples. We prop ...
... In this paper, we present an automated approach to discover patterns that can distinguish between sequences belonging to different labeled groups. Our method searches for approximately conserved motifs that occur with varying statistical properties in positive and negative training examples. We prop ...
A brief introduction to agent mining
... example, in [2], selected papers discuss about mining temporal patterns to improve agent behaviors, and equipping agents with commonsense knowledge acquired from search query logs. In [7], outcomes are reported about a data mining approach to identify obligation norms in agent societies, probabilist ...
... example, in [2], selected papers discuss about mining temporal patterns to improve agent behaviors, and equipping agents with commonsense knowledge acquired from search query logs. In [7], outcomes are reported about a data mining approach to identify obligation norms in agent societies, probabilist ...
Data Mining Methods for Knowledge Discovery in Multi
... minimized1 and the variable vector x = [x1 , x2 , . . . , xn ] belongs to the non-empty feasible region S ⊂ Rn . The feasible region is formed by the constraints of the problem which include the bounds on the variables. A variable vector x1 is said to dominate x2 and is denoted as x1 ≺ x2 if and onl ...
... minimized1 and the variable vector x = [x1 , x2 , . . . , xn ] belongs to the non-empty feasible region S ⊂ Rn . The feasible region is formed by the constraints of the problem which include the bounds on the variables. A variable vector x1 is said to dominate x2 and is denoted as x1 ≺ x2 if and onl ...
Cortina: a web image search engine
... Size: 4.2 billion documents in Google’s index Diversity: Documents in any context, language ...
... Size: 4.2 billion documents in Google’s index Diversity: Documents in any context, language ...
Chapter 1 Introduction to Business Analytics
... 1. k-Nearest Neighbors (k-NN) Algorithm find records in a database that have similar numerical values of a set of predictor variables 2. Discriminant Analysis use predefined classes based on a set of linear discriminant functions of the predictor variables 3. Logistic Regression estimate the probabi ...
... 1. k-Nearest Neighbors (k-NN) Algorithm find records in a database that have similar numerical values of a set of predictor variables 2. Discriminant Analysis use predefined classes based on a set of linear discriminant functions of the predictor variables 3. Logistic Regression estimate the probabi ...
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.