Aalborg Universitet
... Iterative refinement clustering algorithms are widely used in data mining area, but they are sensitive to the initialization. In the past decades, many modified initialization methods have been proposed to reduce the influence of initialization sensitivity problem. The essence of iterative refinement cl ...
... Iterative refinement clustering algorithms are widely used in data mining area, but they are sensitive to the initialization. In the past decades, many modified initialization methods have been proposed to reduce the influence of initialization sensitivity problem. The essence of iterative refinement cl ...
A Literature Review on Kidney Disease Prediction using Data
... form of association rules or some other internal formalism. Support Vector Machine (SVM): Support vector machine (SVM) is an algorithm that attempts to find a linear separator (hyper-plane) between the data points of two classes in multidimensional space. SVMs are well suited to dealing with interac ...
... form of association rules or some other internal formalism. Support Vector Machine (SVM): Support vector machine (SVM) is an algorithm that attempts to find a linear separator (hyper-plane) between the data points of two classes in multidimensional space. SVMs are well suited to dealing with interac ...
Classification fundamentals - DataBase and Data Mining Group
... Database coverage: the training set is covered by selecting topmost rules according to previous sort ...
... Database coverage: the training set is covered by selecting topmost rules according to previous sort ...
a study on integrated approach of data mining and cloud mining
... data brings potentials to discover and utilize valuable knowledge from data. Data mining has been a successful tool to analyze data from different angles and getting useful information from data. It can also help in predicting trends or values, classification of data, categorization of data and to f ...
... data brings potentials to discover and utilize valuable knowledge from data. Data mining has been a successful tool to analyze data from different angles and getting useful information from data. It can also help in predicting trends or values, classification of data, categorization of data and to f ...
clustering large-scale data based on modified affinity propagation
... optimal value of ‘preference’ must be set. The bisection method suggests using AP to find a suitable preference for specified cluster number [20]. The process of finding the parameters is very time consuming because each change of any parameter will require re-running the algorithm. KAP [20] was dev ...
... optimal value of ‘preference’ must be set. The bisection method suggests using AP to find a suitable preference for specified cluster number [20]. The process of finding the parameters is very time consuming because each change of any parameter will require re-running the algorithm. KAP [20] was dev ...
Performance Analysis of Decision Tree Algorithms for Breast Cancer
... Regression Tree (CART) and derived a common method for developing statistical models from simple feature data. CART is powerful since it deals with data that is not fully finished, data with predicated and input features. The tree developed by CART will contain human readable rules. The algorithm wi ...
... Regression Tree (CART) and derived a common method for developing statistical models from simple feature data. CART is powerful since it deals with data that is not fully finished, data with predicated and input features. The tree developed by CART will contain human readable rules. The algorithm wi ...
Using Classification and Visualization on Pattern Databases for Gene Expression Data Analysis
... must be done in cooperation with the end-users. This post-processing is fundamentally needed since data mining algorithms only provide a priori interesting patterns. We have to support queries on such huge pattern databases. Not only we need query languages for that but also efficient query evaluation ...
... must be done in cooperation with the end-users. This post-processing is fundamentally needed since data mining algorithms only provide a priori interesting patterns. We have to support queries on such huge pattern databases. Not only we need query languages for that but also efficient query evaluation ...
data mining and data visualisation
... The visualisation of data encodes digital information into analogue form for easy access by the user. Analogue visualisations are well suited to identifying spatial distances, unlike digital information. For example the physical difference between 79994 and 80000 is substantial, but the difference ...
... The visualisation of data encodes digital information into analogue form for easy access by the user. Analogue visualisations are well suited to identifying spatial distances, unlike digital information. For example the physical difference between 79994 and 80000 is substantial, but the difference ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... with all these public versus private data issues so that analysis modeling process does not infringe on these legal boundaries. Law enforcement agencies like that of police today are faced with large volume of data that must be processed and transformed into useful information. The high volume of cr ...
... with all these public versus private data issues so that analysis modeling process does not infringe on these legal boundaries. Law enforcement agencies like that of police today are faced with large volume of data that must be processed and transformed into useful information. The high volume of cr ...
Data Mining Techniques ACM-SIGMOD`96 Conference Tutorial
... Data categorization based on a set of training objects. – Applications: credit approval, target marketing, medical diagnosis, treatment effectiveness analysis, etc. – Example: classify a set of diseases and provide the symptoms which describe each class or subclass. The classification task: Based on ...
... Data categorization based on a set of training objects. – Applications: credit approval, target marketing, medical diagnosis, treatment effectiveness analysis, etc. – Example: classify a set of diseases and provide the symptoms which describe each class or subclass. The classification task: Based on ...
JMP® 9 and Interactive Statistical Discovery
... Suppose you need to characterize the multivariate distribution of several variables, in this case 30,000 rows of FCS data. The KMeans platform has many features for doing this. The data may be noisy, so as a first step you find the th distance of each point to the k nearest neighbor, as shown in Fig ...
... Suppose you need to characterize the multivariate distribution of several variables, in this case 30,000 rows of FCS data. The KMeans platform has many features for doing this. The data may be noisy, so as a first step you find the th distance of each point to the k nearest neighbor, as shown in Fig ...
Data Mining Techniques ACM-SIGMOD`96 Conference Tutorial
... Data categorization based on a set of training objects. – Applications: credit approval, target marketing, medical diagnosis, treatment effectiveness analysis, etc. – Example: classify a set of diseases and provide the symptoms which describe each class or subclass. The classification task: Based on ...
... Data categorization based on a set of training objects. – Applications: credit approval, target marketing, medical diagnosis, treatment effectiveness analysis, etc. – Example: classify a set of diseases and provide the symptoms which describe each class or subclass. The classification task: Based on ...
International Journal on Advanced Computer Theory and
... Constraints based user clustering In this paper, we define constraints based twitter clustering for user clustering. In case of constraints based clustering, small amount of information is available in the form of Must Link and Cannot Link constraints. Pairs in Must Link constraints should come toge ...
... Constraints based user clustering In this paper, we define constraints based twitter clustering for user clustering. In case of constraints based clustering, small amount of information is available in the form of Must Link and Cannot Link constraints. Pairs in Must Link constraints should come toge ...
Privacy Preserving Data Mining
... Consider a transactional database D involving a set of transactions T. Each transaction involves some items from the set I = {1,2,3,4}. Association Rule Mining is the data mining process involving the identification of sets of items (a.k.a. itemsets) that frequently co-occur in the set of transactio ...
... Consider a transactional database D involving a set of transactions T. Each transaction involves some items from the set I = {1,2,3,4}. Association Rule Mining is the data mining process involving the identification of sets of items (a.k.a. itemsets) that frequently co-occur in the set of transactio ...
085-2013: Using Data Mining in Forecasting Problems
... using specific statistical approaches. Thus, very clean and specific cause and effect models can be built. In contrast, in many business settings a set of “data” often times contains many Y‟s and X‟s, but have no particular modeling objective or hypothesis for being collected in the first place. Thi ...
... using specific statistical approaches. Thus, very clean and specific cause and effect models can be built. In contrast, in many business settings a set of “data” often times contains many Y‟s and X‟s, but have no particular modeling objective or hypothesis for being collected in the first place. Thi ...
DSS Chapter 1
... hierarchical and nonhierarchical), such as k-means, k-modes, and so on Neural networks (adaptive resonance theory [ART], self-organizing map [SOM]) Fuzzy logic (e.g., fuzzy c-means algorithm) Genetic algorithms ...
... hierarchical and nonhierarchical), such as k-means, k-modes, and so on Neural networks (adaptive resonance theory [ART], self-organizing map [SOM]) Fuzzy logic (e.g., fuzzy c-means algorithm) Genetic algorithms ...
Discovering Similar Patterns in Time Series
... There is a growing need to search databases for data time series that resemble a particular one. For example, it could be a matter of finding companies with a similar growth pattern or discovering products with similar sales patterns. One important question is to decide what similarity means. The si ...
... There is a growing need to search databases for data time series that resemble a particular one. For example, it could be a matter of finding companies with a similar growth pattern or discovering products with similar sales patterns. One important question is to decide what similarity means. The si ...
Web Search Result Optimization using Association Rule Mining
... Step 5: These item sets are then used to generate association rules which have threshold values less than or equal to confidence values. Step 6: It first creates the rules for frequent item sets and then for subsets is created recursively. Associative classification method uses association rules for ...
... Step 5: These item sets are then used to generate association rules which have threshold values less than or equal to confidence values. Step 6: It first creates the rules for frequent item sets and then for subsets is created recursively. Associative classification method uses association rules for ...
DECISION SUPPORT IN DATA MINING USING ROUGH SET THEORY
... pattern recognition, statistics, visualization and others. Decision support provides a selection of data analysis, simulation, visualization and modeling techniques, and software tools such as decision support systems, group decision support and mediation systems, expert systems, databases and data ...
... pattern recognition, statistics, visualization and others. Decision support provides a selection of data analysis, simulation, visualization and modeling techniques, and software tools such as decision support systems, group decision support and mediation systems, expert systems, databases and data ...
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.