Analysis of Student Result Using Clustering Techniques
... The researchers may use the model to identify the existing area of research in the field of data mining in higher educational system. In this study we make a use of data mining process in a student’s database using K-means clustering algorithm to predict students result. We hope that the information ...
... The researchers may use the model to identify the existing area of research in the field of data mining in higher educational system. In this study we make a use of data mining process in a student’s database using K-means clustering algorithm to predict students result. We hope that the information ...
Heart Disease Diagnosis Using Predictive Data Mining
... interpret, robust, perform well with large datasets, able to handle both numerical and categorical data. Decision-tree learners can create over-complex trees that do not generalise well from the training data is one the limitation. C. Clustering Clustering is a process of partitioning a set of data ...
... interpret, robust, perform well with large datasets, able to handle both numerical and categorical data. Decision-tree learners can create over-complex trees that do not generalise well from the training data is one the limitation. C. Clustering Clustering is a process of partitioning a set of data ...
CS3056365
... high-dimensional data is a particularly important task in cluster analysis because many applications require the analysis of objects containing a large number of features or dimensions[5].CLUIQE algorithm can be used for such a high dimensional data. CLIQUE method searches for clusters in subspaces ...
... high-dimensional data is a particularly important task in cluster analysis because many applications require the analysis of objects containing a large number of features or dimensions[5].CLUIQE algorithm can be used for such a high dimensional data. CLIQUE method searches for clusters in subspaces ...
A Fuzzy Subspace Algorithm for Clustering High Dimensional Data
... The idea behind dimension reduction approaches and feature selection approaches is to first reduce the dimensionality of the original data set by removing less important variables or by transforming the original data set into one in a low dimensional space, and then apply conventional clustering algo ...
... The idea behind dimension reduction approaches and feature selection approaches is to first reduce the dimensionality of the original data set by removing less important variables or by transforming the original data set into one in a low dimensional space, and then apply conventional clustering algo ...
Data Mining - Knowledge Discovery, Data Warehousing
... • dates are transformed internally to a standard value Frequently, just the year (YYYY) is sufficient For more details, we may need the month, the day, the hour, etc Representing date as YYYYMM or YYYYMMDD can be OK, but has problems Q: What are the problems with YYYYMMDD dates? • A: Ignoring for no ...
... • dates are transformed internally to a standard value Frequently, just the year (YYYY) is sufficient For more details, we may need the month, the day, the hour, etc Representing date as YYYYMM or YYYYMMDD can be OK, but has problems Q: What are the problems with YYYYMMDD dates? • A: Ignoring for no ...
DRID- A New Merging Approach - International Journal of Computer
... And in case of grid based environment when we entered a set of hundered array as an input value then the output of the algorithm assigns a Same ClusterID for ...
... And in case of grid based environment when we entered a set of hundered array as an input value then the output of the algorithm assigns a Same ClusterID for ...
Data Mining - Cluster Analysis
... Scalability - We need highly scalable clustering algorithms to deal with large databases. Ability to deal with different kind of attributes - Algorithms should be capable to be applied on any kind of data such as interval based (numerical) data, categorical, binary data. Discovery of clusters with a ...
... Scalability - We need highly scalable clustering algorithms to deal with large databases. Ability to deal with different kind of attributes - Algorithms should be capable to be applied on any kind of data such as interval based (numerical) data, categorical, binary data. Discovery of clusters with a ...
176. weather forecast prediction: a data mining application
... layers, number of nodes in each layer and their connectivity. The numbers of output nodes are fixed by the quantities to be estimated. The number of input nodes is dependent on the problem under consideration and the modeler’s discretion to utilize domain knowledge. The number of neurons in the hidd ...
... layers, number of nodes in each layer and their connectivity. The numbers of output nodes are fixed by the quantities to be estimated. The number of input nodes is dependent on the problem under consideration and the modeler’s discretion to utilize domain knowledge. The number of neurons in the hidd ...
chapter 4 survey of data mining techniques
... representation. Portnoy et al [51] present a method for detecting intrusions based on feature vectors collected from the network, without being given any information about classifications of these vectors. They designed a system that implemented this method, and it was able to detect a large number ...
... representation. Portnoy et al [51] present a method for detecting intrusions based on feature vectors collected from the network, without being given any information about classifications of these vectors. They designed a system that implemented this method, and it was able to detect a large number ...
Almah Saaid, Robert King, Darfiana Nur
... clusters, normal and fraud, respectively. In data mining, unsupervised learning problems can be treated as supervised learning by using the following ideas [15]: (i) to create a class label for the observed data (the original unlabeled data) and (ii) to create another class label for synthetic data ...
... clusters, normal and fraud, respectively. In data mining, unsupervised learning problems can be treated as supervised learning by using the following ideas [15]: (i) to create a class label for the observed data (the original unlabeled data) and (ii) to create another class label for synthetic data ...
Extensible Clustering Algorithms for Metric Space
... sometimes up to a large number, e.g., ten. In this paper, we propose gradual clustering algorithms, which progressively clusters objects from a small number to a possibly large one. We use the triangle inequality for reducing the number of distance calculations in metric spaces. The basic idea is to ...
... sometimes up to a large number, e.g., ten. In this paper, we propose gradual clustering algorithms, which progressively clusters objects from a small number to a possibly large one. We use the triangle inequality for reducing the number of distance calculations in metric spaces. The basic idea is to ...
An analytic approach to select data mining for business decision
... Data mining methods refer to the function types that data mining tools provide. The conceptual definition of each data mining method and the assortment basis always differ for the ease of explanation, the consideration of present situation, or researcher background. Classification, association, predic ...
... Data mining methods refer to the function types that data mining tools provide. The conceptual definition of each data mining method and the assortment basis always differ for the ease of explanation, the consideration of present situation, or researcher background. Classification, association, predic ...
Data Mining
... nearest record is a great distance from the unclassified record. • The degree of homogeneity amongst the predictions within the K nearest neighbors can also be used. If all the nearest neighbors make the same prediction then there is much higher confidence in the prediction than if half the records ...
... nearest record is a great distance from the unclassified record. • The degree of homogeneity amongst the predictions within the K nearest neighbors can also be used. If all the nearest neighbors make the same prediction then there is much higher confidence in the prediction than if half the records ...
CS1250104
... independent. The classifier computes the probability of each attribute in a class. The result of the classification is the class with the highest posterior probability. Posterior probability is proportional to product of prior probability & like hood. Naïve Bayes main strength is its simplicity, eff ...
... independent. The classifier computes the probability of each attribute in a class. The result of the classification is the class with the highest posterior probability. Posterior probability is proportional to product of prior probability & like hood. Naïve Bayes main strength is its simplicity, eff ...
Scalable Cluster Analysis of Spatial Events
... up to 10 minutes while very long clusters are rare. We interactively filter out clusters with durations below 10 minutes and consider the remaining 3,513 clusters as representing traffic jams. We investigate the temporal distribution of these traffic jams by means of two-dimensional (2D) histograms ...
... up to 10 minutes while very long clusters are rare. We interactively filter out clusters with durations below 10 minutes and consider the remaining 3,513 clusters as representing traffic jams. We investigate the temporal distribution of these traffic jams by means of two-dimensional (2D) histograms ...
pptx - Computer Science and Engineering
... Experiments showed that exact methods can rarely outperform the sequential scan when dimensionality exceeds ten ...
... Experiments showed that exact methods can rarely outperform the sequential scan when dimensionality exceeds ten ...
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.