Trust-but-Verify: Verifying Result Correctness of Outsourced
... however, raises a serious security issue: how can the client of weak computational power verify that the server returned correct mining result? In this paper, we focus on the specific task of frequent item set mining. We consider the server that is potentially untrusted and tries to escape from veri ...
... however, raises a serious security issue: how can the client of weak computational power verify that the server returned correct mining result? In this paper, we focus on the specific task of frequent item set mining. We consider the server that is potentially untrusted and tries to escape from veri ...
Density-Based Clustering over an Evolving Data Stream with Noise
... environment observation, the layout of an area with similar environment conditions could be any shape. 3. Ability to handle outliers. In the data stream scenario, due to the influence of various factors, such as electromagnetic interference, temporary failure of sensors, weak battery of sensors, etc ...
... environment observation, the layout of an area with similar environment conditions could be any shape. 3. Ability to handle outliers. In the data stream scenario, due to the influence of various factors, such as electromagnetic interference, temporary failure of sensors, weak battery of sensors, etc ...
A Parallel Attribute Reduction Algorithm based on Affinity
... cluster analysis has been widely used in natural and social science. It classifies some objects into several clusters, making the differences of the objects in distinct classes as large as possible while in the same as small as possible. The most essential point—“clustering” is found among samples. ...
... cluster analysis has been widely used in natural and social science. It classifies some objects into several clusters, making the differences of the objects in distinct classes as large as possible while in the same as small as possible. The most essential point—“clustering” is found among samples. ...
bnd - Purdue University
... This can be done by sorting s in descending order and assigning 1’s to components of x in a greedy fashion Optimistic, works well on very sparse data ...
... This can be done by sorting s in descending order and assigning 1’s to components of x in a greedy fashion Optimistic, works well on very sparse data ...
network traffic clustering and geographic visualization
... To get around these obstacles, one proposal is to characterize network traffic based on features of the transport-layer statistics irrespective of port-based identification or payload content. The idea here is that different applications on the network will exhibit different patterns of behavior wh ...
... To get around these obstacles, one proposal is to characterize network traffic based on features of the transport-layer statistics irrespective of port-based identification or payload content. The idea here is that different applications on the network will exhibit different patterns of behavior wh ...
Data mining
... Finds the probability of the relationship between all input and predictable columns. This algorithm is useful for quickly generating mining models to discover relationships. Supports only discrete or discretized attributes. Treats all input attributes as independent. Analyzes the factors that contri ...
... Finds the probability of the relationship between all input and predictable columns. This algorithm is useful for quickly generating mining models to discover relationships. Supports only discrete or discretized attributes. Treats all input attributes as independent. Analyzes the factors that contri ...
logical framework design of online educational system (oes)
... stages such as intelligent studying, analysis and recommendation. First the system records all information of studying phase and test the result and so on to get the interest, ability technique of user. Then the system turns on analysis of this knowledge and makes the suitable study technique, respe ...
... stages such as intelligent studying, analysis and recommendation. First the system records all information of studying phase and test the result and so on to get the interest, ability technique of user. Then the system turns on analysis of this knowledge and makes the suitable study technique, respe ...
Hard and fuzzy k-Modes algorithms
... the k-means algorithm [Ralambondrainy, 1995] hierarchical clustering methods [Gower, 1991] the PAM algorithm [Kaufman et al, 1990] the fuzzy-statistical algorithms [Woodbury, 1974] The conceptual clustering methods [Michalski, 1983] ...
... the k-means algorithm [Ralambondrainy, 1995] hierarchical clustering methods [Gower, 1991] the PAM algorithm [Kaufman et al, 1990] the fuzzy-statistical algorithms [Woodbury, 1974] The conceptual clustering methods [Michalski, 1983] ...
Page 1 of 2 COMP 3110 Data Mining And Knowledge Discovery (3
... Attitude Build up team spirit in solving challenging data mining problems ...
... Attitude Build up team spirit in solving challenging data mining problems ...
Density-Based Clustering over an Evolving Data Stream with Noise
... environment observation, the layout of an area with similar environment conditions could be any shape. 3. Ability to handle outliers. In the data stream scenario, due to the influence of various factors, such as electromagnetic interference, temporary failure of sensors, weak battery of sensors, etc ...
... environment observation, the layout of an area with similar environment conditions could be any shape. 3. Ability to handle outliers. In the data stream scenario, due to the influence of various factors, such as electromagnetic interference, temporary failure of sensors, weak battery of sensors, etc ...
Spatio-Temporal Clustering: a Survey
... Another approach to cluster complex form of data, like trajectories, is to transform the complex objects into features vectors, i.e. a set of multidimensional vectors where each dimension represents a single characteristic of the original object, and then to cluster them using generic clustering alg ...
... Another approach to cluster complex form of data, like trajectories, is to transform the complex objects into features vectors, i.e. a set of multidimensional vectors where each dimension represents a single characteristic of the original object, and then to cluster them using generic clustering alg ...
An Analysis of Algorithms Used by Business Intelligence Software
... Business intelligence (BI) has gradually become a popular information systems terminology. There are a variety of BI software packages in the market today although it is actually a combination of data mining, statistical analysis, and advanced reporting features. Data mining searches for hidden patt ...
... Business intelligence (BI) has gradually become a popular information systems terminology. There are a variety of BI software packages in the market today although it is actually a combination of data mining, statistical analysis, and advanced reporting features. Data mining searches for hidden patt ...
pdf
... robotics, tumour classification, computer vision, etc. [2]. WEKA (Waikato Environment for Knowledge Analysis) is a machine learning tool kit developed by Waikato University of New Zealand [3]. Weka is an open-source software distributed under GNU Public License and is widely used by researchers, sta ...
... robotics, tumour classification, computer vision, etc. [2]. WEKA (Waikato Environment for Knowledge Analysis) is a machine learning tool kit developed by Waikato University of New Zealand [3]. Weka is an open-source software distributed under GNU Public License and is widely used by researchers, sta ...
Using Clustering Methods in Geospatial
... generalization to the non-spatial attributes to produce a number of generalized tuples. Then, for each such generalized tuple, all spatial components are collected and clustered using CLARANS. CLARANS suffers from some weaknesses [Ng and Han 1994]. First, it assumes that the points to be clustered a ...
... generalization to the non-spatial attributes to produce a number of generalized tuples. Then, for each such generalized tuple, all spatial components are collected and clustered using CLARANS. CLARANS suffers from some weaknesses [Ng and Han 1994]. First, it assumes that the points to be clustered a ...
CS 536-Data_Mining_Spring 2010-11
... Data mining or the discovery of knowledge in large datasets has created a lot of interest in the database and data engineering communities in recent years. The tremendous increase in the generation and collection of data has highlighted the urgent need for systems that can extract useful and actiona ...
... Data mining or the discovery of knowledge in large datasets has created a lot of interest in the database and data engineering communities in recent years. The tremendous increase in the generation and collection of data has highlighted the urgent need for systems that can extract useful and actiona ...
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.