Comparison of K-means and Backpropagation Data Mining Algorithms
... is observed, that the dataset consists of more than 60% of records to be in the rejected category. Hence the machine learning algorithms were very excellent in recognizing the rejected data however they were not able to identify selected records to a large extent. Therefore the dataset was premedita ...
... is observed, that the dataset consists of more than 60% of records to be in the rejected category. Hence the machine learning algorithms were very excellent in recognizing the rejected data however they were not able to identify selected records to a large extent. Therefore the dataset was premedita ...
Extraction of Best Attribute Subset using Kruskal`s Algorithm
... expanding learning accuracy, furthermore, enhancing result comprehensibility [1], [4]. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same cluster are more similar to each other than to those in other clusters. It is a main task of explorato ...
... expanding learning accuracy, furthermore, enhancing result comprehensibility [1], [4]. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same cluster are more similar to each other than to those in other clusters. It is a main task of explorato ...
Computational Geometry and Spatial Data Mining
... • Intuition: We use the in different places – Snapping points – Trying only circle centers on grid points ...
... • Intuition: We use the in different places – Snapping points – Trying only circle centers on grid points ...
Extensions to the k-Means Algorithm for Clustering Large Data Sets
... algorithms still target on numeric data and cannot be used to solve massive categorical data clustering problems. In this paper we present two new algorithms that use the k-means paradigm to cluster data having categorical values. The k-modes algorithm (Huang, 1997b) extends the k-means paradigm to ...
... algorithms still target on numeric data and cannot be used to solve massive categorical data clustering problems. In this paper we present two new algorithms that use the k-means paradigm to cluster data having categorical values. The k-modes algorithm (Huang, 1997b) extends the k-means paradigm to ...
From Design to Implementation Sections 5.4, 5.5 and 5.7
... – Partitioning the input data set into subsets (clusters), so that data in each subset share common aspects. The partitioning is often indicated by a similarity measure implemented by a distance ...
... – Partitioning the input data set into subsets (clusters), so that data in each subset share common aspects. The partitioning is often indicated by a similarity measure implemented by a distance ...
Towards Cohesive Anomaly Mining Yun Xiong Yangyong Zhu Philip S. Yu
... Dubes 1988; Bohm et al. 2010). Although the density-based methods, such as DBSCAN (Ester et al. 1996) and DENCLUE (Hinneburg and Keim 1998), can cluster a subset of data into multiple dense regions, those methods are often sensitive to many parameters – using different parameter values often give dr ...
... Dubes 1988; Bohm et al. 2010). Although the density-based methods, such as DBSCAN (Ester et al. 1996) and DENCLUE (Hinneburg and Keim 1998), can cluster a subset of data into multiple dense regions, those methods are often sensitive to many parameters – using different parameter values often give dr ...
OUTLIER DETECTION USING ENHANCED K
... Outlier detection can be mostly classified into three groups. First is the distance based outlier detection, it detects the outlier from the neighborhood points. Second is the density based outlier detection, here it detects the local outlier from the neighborhood based on the density or the number ...
... Outlier detection can be mostly classified into three groups. First is the distance based outlier detection, it detects the outlier from the neighborhood points. Second is the density based outlier detection, here it detects the local outlier from the neighborhood based on the density or the number ...
Business Analytics crash course on Data Mining, Predictive Modeling
... This course will change the way you think about data and its role in business. Increasingly, decisionmakers and systems rely on intelligent tools and techniques to analyze data systematically to improve decision-making. We will examine how data analysis technologies can be used to improve decision m ...
... This course will change the way you think about data and its role in business. Increasingly, decisionmakers and systems rely on intelligent tools and techniques to analyze data systematically to improve decision-making. We will examine how data analysis technologies can be used to improve decision m ...
Data Mining and Big Data Science
... Master of Science in Computer Science Exchange Programme in Computer Science (master's level) ...
... Master of Science in Computer Science Exchange Programme in Computer Science (master's level) ...
Chapter 9 - cse.sc.edu
... Adapt to the characteristics of the data set to find the natural clusters Use a dynamic model to measure the similarity between clusters – Main property is the relative closeness and relative interconnectivity of the cluster – Two clusters are combined if the resulting cluster shares certain propert ...
... Adapt to the characteristics of the data set to find the natural clusters Use a dynamic model to measure the similarity between clusters – Main property is the relative closeness and relative interconnectivity of the cluster – Two clusters are combined if the resulting cluster shares certain propert ...
Document
... From the technical standpoint, there are at least three kinds of use that people have in mind: Forensic search Signature detection, for various prespecified signals Prospective data mining for anomalies ...
... From the technical standpoint, there are at least three kinds of use that people have in mind: Forensic search Signature detection, for various prespecified signals Prospective data mining for anomalies ...
Finding Behavior Patterns from Temporal Data using
... data given models of the set of clusters (Smyth 1997). Since our distance measure does well in maximizing the homogeneity of objects within each cluster, we want a criterion measure that is good at comparing partitions in terms of their between-cluster distances. We use the Partition Mutual Informat ...
... data given models of the set of clusters (Smyth 1997). Since our distance measure does well in maximizing the homogeneity of objects within each cluster, we want a criterion measure that is good at comparing partitions in terms of their between-cluster distances. We use the Partition Mutual Informat ...
11ClusAdvanced
... NEps(p): {q belongs to D | dist(p,q) ≤ Eps} Directly density-reachable: A point p is directly densityreachable from a point q w.r.t. Eps, MinPts if ...
... NEps(p): {q belongs to D | dist(p,q) ≤ Eps} Directly density-reachable: A point p is directly densityreachable from a point q w.r.t. Eps, MinPts if ...
Concept Ontology for Text Classification
... Each corresponds to some split in classification hierarchy The key insight is that each subtask is significantly simpler than original task The classifier at each node in the hierarchy needs to be distinguished between small number of categories This is possible using small set of features ...
... Each corresponds to some split in classification hierarchy The key insight is that each subtask is significantly simpler than original task The classifier at each node in the hierarchy needs to be distinguished between small number of categories This is possible using small set of features ...
8.Testing models built
... corresponding to known outputs exists, a model can be tested – in principle – by splitting the available data into two parts: the training set and test set. The split is made randomly so that we may assume both sets to follow the class distribution of the original population of the data. The former ...
... corresponding to known outputs exists, a model can be tested – in principle – by splitting the available data into two parts: the training set and test set. The split is made randomly so that we may assume both sets to follow the class distribution of the original population of the data. The former ...
SOM in data mining
... However, in order to be really useful, clustering needs to be an automated process. When clusters are identified visually the results may be different when performed by different people. There are several techniques which can be used to cluster the SOM autonomously, but the results they provide do n ...
... However, in order to be really useful, clustering needs to be an automated process. When clusters are identified visually the results may be different when performed by different people. There are several techniques which can be used to cluster the SOM autonomously, but the results they provide do n ...
Mining Regional Knowledge in Spatial Dataset
... 1. Finding regions on planet Mars where shallow and deep ice are co-located, using point and raster datasets. In figure 1, regions in red have very high colocation and regions in blue have anti co-location. 2. Finding co-location patterns involving chemical concentrations with values on the wings of ...
... 1. Finding regions on planet Mars where shallow and deep ice are co-located, using point and raster datasets. In figure 1, regions in red have very high colocation and regions in blue have anti co-location. 2. Finding co-location patterns involving chemical concentrations with values on the wings of ...
Cluster Analysis on High-Dimensional Data: A Comparison of
... Laflamme, 2009) and Support Vector Machine-based approach (SVM) (W. Chang, Zeng, & Chen, 2005). Kmeans technique is probably the most popular and is a simple solution for clustering. However, the weakness with this technique is in determining the proper number of clusters and potential to being trap ...
... Laflamme, 2009) and Support Vector Machine-based approach (SVM) (W. Chang, Zeng, & Chen, 2005). Kmeans technique is probably the most popular and is a simple solution for clustering. However, the weakness with this technique is in determining the proper number of clusters and potential to being trap ...
Cluster analysis
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It will often be necessary to modify data preprocessing and model parameters until the result achieves the desired properties.Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek βότρυς ""grape"") and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals.Cluster analysis was originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Zubin in 1938 and Robert Tryon in 1939 and famously used by Cattell beginning in 1943 for trait theory classification in personality psychology.