Slides
... informal definition two problems 1. similarity search problem given a set X of objects (off-line) given a query object q (query time) find the object in X that is most similar to q 2. all-pairs similarity problem given a set X of objects (off-line) find all pairs of objects in X that are similar ...
... informal definition two problems 1. similarity search problem given a set X of objects (off-line) given a query object q (query time) find the object in X that is most similar to q 2. all-pairs similarity problem given a set X of objects (off-line) find all pairs of objects in X that are similar ...
DM_05_03_Bayesian Cl.. - Iust personal webpages
... – Using the Laplacian correction for the three quantities, we pretend that we have 1 more instance for each ...
... – Using the Laplacian correction for the three quantities, we pretend that we have 1 more instance for each ...
Anonymizing Classification Data for Privacy Preservation
... or some prefers recall, whereas the others prefer precision, and so on. In other cases, the recipient may not know exactly what to do before seeing the data, such as visual data mining, where the human makes decisions based on certain distributions of data records at each step. Publishing the data p ...
... or some prefers recall, whereas the others prefer precision, and so on. In other cases, the recipient may not know exactly what to do before seeing the data, such as visual data mining, where the human makes decisions based on certain distributions of data records at each step. Publishing the data p ...
Report - UF CISE - University of Florida
... location problems. These include the Euclidean k-medians in which the objective is to minimize the sum of distances to the nearest center and the geometric k-center problem in which the objective is to minimize the maximum distance from every point to its closest center. There are no efficient solut ...
... location problems. These include the Euclidean k-medians in which the objective is to minimize the sum of distances to the nearest center and the geometric k-center problem in which the objective is to minimize the maximum distance from every point to its closest center. There are no efficient solut ...
Efficient Pattern Mining from Temporal Data through
... same range are represented by one symbol taken from an alphabet set. Basically, three types of periodic patterns [10] can be detected in a time series: 1) symbol periodicity, 2) sequence periodicity or partial periodic patterns, and 3) segment or full-cycle periodicity. A time series is said to have ...
... same range are represented by one symbol taken from an alphabet set. Basically, three types of periodic patterns [10] can be detected in a time series: 1) symbol periodicity, 2) sequence periodicity or partial periodic patterns, and 3) segment or full-cycle periodicity. A time series is said to have ...
Decision Tree, Naive Bayes, Bayesian Networks
... Model construction: describing a set of predetermined classes Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute The set of tuples used for model construction is training set The model is represented as classification rules, decision trees, ...
... Model construction: describing a set of predetermined classes Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute The set of tuples used for model construction is training set The model is represented as classification rules, decision trees, ...
Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints
... would lead to an intense analysis of the intrusion by human experts in order to understand its cause, find a remedy, and make the system more secure. Note that in our technique we consider mining from only one stream. We address the infinite length problem by dividing the stream into equal-sized chu ...
... would lead to an intense analysis of the intrusion by human experts in order to understand its cause, find a remedy, and make the system more secure. Note that in our technique we consider mining from only one stream. We address the infinite length problem by dividing the stream into equal-sized chu ...
Density-Based Clustering over an Evolving Data Stream with Noise
... Recently, the clustering of data streams has been attracting a lot of research attention. Previous methods, one-pass [4, 10, 11] or evolving [1, 2, 5, 18], do not consider that the clusters in data streams could be of arbitrary shape. In particular, their results are often spherical clusters. One-pa ...
... Recently, the clustering of data streams has been attracting a lot of research attention. Previous methods, one-pass [4, 10, 11] or evolving [1, 2, 5, 18], do not consider that the clusters in data streams could be of arbitrary shape. In particular, their results are often spherical clusters. One-pa ...
Idescat. SORT. Correspondence analysis and two
... Correspondence analysis (CA), as other biplot techniques, offers the remarkable feature of jointly representing individuals and variables. As a result of such analyses, not only does one gain insight in the relationship amongst individuals and amongst variables, but one can also find an indication of ...
... Correspondence analysis (CA), as other biplot techniques, offers the remarkable feature of jointly representing individuals and variables. As a result of such analyses, not only does one gain insight in the relationship amongst individuals and amongst variables, but one can also find an indication of ...
ppt - hkust cse
... Y3=s0: Same attitude toward C-Gov and C-Bus People who are touch on corruption are equally tough toward C-Gov and C-Bus. People who are relaxed about corruption are more relaxed toward C-Bus than C-GOv ...
... Y3=s0: Same attitude toward C-Gov and C-Bus People who are touch on corruption are equally tough toward C-Gov and C-Bus. People who are relaxed about corruption are more relaxed toward C-Bus than C-GOv ...
Probabilistic Discovery of Time Series Motifs
... subsequence C1 that has highest count of non-trivial matches (ties are broken by choosing the motif whose matches have the lower variance). The Kth most significant motif in T (hereafter called the K-Motif(n,R) ) is the subsequence CK that has the highest count of non-trivial matches, and satisfies ...
... subsequence C1 that has highest count of non-trivial matches (ties are broken by choosing the motif whose matches have the lower variance). The Kth most significant motif in T (hereafter called the K-Motif(n,R) ) is the subsequence CK that has the highest count of non-trivial matches, and satisfies ...
Scalable High Performance Dimension Reduction
... Future Works Hybrid Parallel MDS MPI-Thread parallel model for MDS ...
... Future Works Hybrid Parallel MDS MPI-Thread parallel model for MDS ...
frequent correlated periodic pattern mining for large volume set
... symbols are the 20 amino acids. Let S = {S1,S2,…,SN} of input time series sequences over a finite symbol set ∑ with |Σ| = R, such that |si| = L, 1≤i≤N and positive integer d and q such that 0≤d≤L and 1≤q≤N. Here given parameters N and L are the number and length of given input sequence. Let a is cal ...
... symbols are the 20 amino acids. Let S = {S1,S2,…,SN} of input time series sequences over a finite symbol set ∑ with |Σ| = R, such that |si| = L, 1≤i≤N and positive integer d and q such that 0≤d≤L and 1≤q≤N. Here given parameters N and L are the number and length of given input sequence. Let a is cal ...
Clustering Approaches for Financial Data Analysis: a Survey
... perform association analysis to identify important factors and eliminate irrelevant ones. B. Classification Classification is another DM approach, which assigns objects to one of the predefined categories. It uses training examples, such as pairs of input and output targets, to find an appropriate t ...
... perform association analysis to identify important factors and eliminate irrelevant ones. B. Classification Classification is another DM approach, which assigns objects to one of the predefined categories. It uses training examples, such as pairs of input and output targets, to find an appropriate t ...
Mining bioprocess data: opportunities and challenges
... Training and test set: training set comprises the process data from a set of runs with known outcomes, which are used to construct a model. The model is assessed by a test set, which is a set of runs (with known outcomes) that were not used for model construction. Data pre-processing methods Adaptiv ...
... Training and test set: training set comprises the process data from a set of runs with known outcomes, which are used to construct a model. The model is assessed by a test set, which is a set of runs (with known outcomes) that were not used for model construction. Data pre-processing methods Adaptiv ...
On the Effect of Endpoints on Dynamic Time Warping
... measurements, such as Quantified Self and Internet of Things [23], time series data are becoming ubiquitous even in our quotidian lives. It is increasingly difficult to think of a human interest or endeavor, from medicine to astronomy, that does not produce copious amounts of time series. Among all ...
... measurements, such as Quantified Self and Internet of Things [23], time series data are becoming ubiquitous even in our quotidian lives. It is increasingly difficult to think of a human interest or endeavor, from medicine to astronomy, that does not produce copious amounts of time series. Among all ...
An Intelligent Hybrid Approach for Improving Recall in Electronic Discovery
... Our technique has been derived from the two algorithms discussed above using WordNet [7] as the knowledge resource. We have modified the original Lesk algorithm adopting WordNet lexical and semantic taxonomy and direct implementation of the Jiang & Conrath algorithm using all the words in context as ...
... Our technique has been derived from the two algorithms discussed above using WordNet [7] as the knowledge resource. We have modified the original Lesk algorithm adopting WordNet lexical and semantic taxonomy and direct implementation of the Jiang & Conrath algorithm using all the words in context as ...
Rare Event Detection in a Spatiotemporal Environment
... previous techniques will not function well. We view the following list as a minimum list of requirements of any rare event detection approach in a spatiotemporal environment: • Supervised and unsupervised: In a target environment domain experts will probably have some a priori ideas concerning what ...
... previous techniques will not function well. We view the following list as a minimum list of requirements of any rare event detection approach in a spatiotemporal environment: • Supervised and unsupervised: In a target environment domain experts will probably have some a priori ideas concerning what ...
K-nearest neighbors algorithm
In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors.k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms.Both for classification and regression, it can be useful to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. For example, a common weighting scheme consists in giving each neighbor a weight of 1/d, where d is the distance to the neighbor.The neighbors are taken from a set of objects for which the class (for k-NN classification) or the object property value (for k-NN regression) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required.A shortcoming of the k-NN algorithm is that it is sensitive to the local structure of the data. The algorithm has nothing to do with and is not to be confused with k-means, another popular machine learning technique.