Improving Digital Forensics Through Data Mining
... best with hyper-spherical cluster shapes; number of clusters and initial seed value need to be specified beforehand; converges to local optima), other clustering algorithms with better features tend to be much more expensive [16]. Our focus in this study is to investigate and demonstrate how cluster ...
... best with hyper-spherical cluster shapes; number of clusters and initial seed value need to be specified beforehand; converges to local optima), other clustering algorithms with better features tend to be much more expensive [16]. Our focus in this study is to investigate and demonstrate how cluster ...
Data Mining and Statistical Inference in Selective Laser Melting
... C. Kamath, B. El-dasher, G. F. Gallegos, W. E. King, and A. Sisto, ”Density of additively-manufactured, 316L SS parts using laser powder-bed fusion at powers up to 400 W,”. Int J Adv Manuf Technol. Volume 74, Issue 1 (2014), Page 65-78. C. Kamath, “On the use of data mining techniques to build high- ...
... C. Kamath, B. El-dasher, G. F. Gallegos, W. E. King, and A. Sisto, ”Density of additively-manufactured, 316L SS parts using laser powder-bed fusion at powers up to 400 W,”. Int J Adv Manuf Technol. Volume 74, Issue 1 (2014), Page 65-78. C. Kamath, “On the use of data mining techniques to build high- ...
Airavat: Security and Privacy for MapReduce
... Range of mapper outputs must be declared in advance Used to estimate “sensitivity” (how much does a single input influence the output?) Determines how much noise is added to outputs to ensure differential privacy ...
... Range of mapper outputs must be declared in advance Used to estimate “sensitivity” (how much does a single input influence the output?) Determines how much noise is added to outputs to ensure differential privacy ...
Exploration of Deep Web Repositories
... middle step of sample generation. A key advantage here is that unlike in the sampling case where many retrieved elements may have to be rejected for skew removal, all retrieved elements may be used, albeit in a weighted fashion, for aggregate estimations. Form-like Search or Hierarchical Browsing In ...
... middle step of sample generation. A key advantage here is that unlike in the sampling case where many retrieved elements may have to be rejected for skew removal, all retrieved elements may be used, albeit in a weighted fashion, for aggregate estimations. Form-like Search or Hierarchical Browsing In ...
Data Mining at Yasuda
... and Grayson (APQC) Sharing knowledge is 90% culture, 5% technology and the rest is magic - Bob Buckman of Buckman Laboratories We live in an increasingly data rich, knowledge poor society (Luan) KM is to bring people to people and people to knowledge (Serban and Luan) ...
... and Grayson (APQC) Sharing knowledge is 90% culture, 5% technology and the rest is magic - Bob Buckman of Buckman Laboratories We live in an increasingly data rich, knowledge poor society (Luan) KM is to bring people to people and people to knowledge (Serban and Luan) ...
as a PDF
... Hierarchical and partition is a clustering method, in the partitioning method required the number of clusters as a input while hierarchical clustering method are no need to number of cluster as a input, so unknown data set given as a input. Hierarchical clustering contains two methods top-down and b ...
... Hierarchical and partition is a clustering method, in the partitioning method required the number of clusters as a input while hierarchical clustering method are no need to number of cluster as a input, so unknown data set given as a input. Hierarchical clustering contains two methods top-down and b ...
extending spatial hot spot detection techniques to
... within distance h and time interval t, or be 0 otherwise. The more a pair of events are close in space and time, the higher the estimated value of K(h,t) will be. Therefore, it provides a measure of space-time interaction. No interaction between space and time can be formulated as K(h)K(t), thus by ...
... within distance h and time interval t, or be 0 otherwise. The more a pair of events are close in space and time, the higher the estimated value of K(h,t) will be. Therefore, it provides a measure of space-time interaction. No interaction between space and time can be formulated as K(h)K(t), thus by ...
To Study the Various Methods used in Data Mining
... analysis of large data sets We consider some methods used in data mining, concentrating on level wise search for all frequently occurring patterns. We show how this technique can be used in various applications. We also discuss possibilities for compiling data mining queries into algorithms, and loo ...
... analysis of large data sets We consider some methods used in data mining, concentrating on level wise search for all frequently occurring patterns. We show how this technique can be used in various applications. We also discuss possibilities for compiling data mining queries into algorithms, and loo ...
Revealing True Subspace Clusters in High Dimensions
... of their hyper-rectangular kernels. Furthermore, both of the clusters should have a large percentage of data points falling within the kernel. This is determined by function Q. If two clusters do not have a boundary intersection in any dimension of their subspaces, the adhesion strength is 0 (Figure ...
... of their hyper-rectangular kernels. Furthermore, both of the clusters should have a large percentage of data points falling within the kernel. This is determined by function Q. If two clusters do not have a boundary intersection in any dimension of their subspaces, the adhesion strength is 0 (Figure ...
Classification rules + time = Temporal Rules
... the subjective parameter that is the user-specified tolerance . 2. Clustering/Classification. In this direction, researchers studied optimal algorithms for clustering/classifying sub-sequences of time series into groups/classes of similar sub-sequences. Different techniques were developed, as the H ...
... the subjective parameter that is the user-specified tolerance . 2. Clustering/Classification. In this direction, researchers studied optimal algorithms for clustering/classifying sub-sequences of time series into groups/classes of similar sub-sequences. Different techniques were developed, as the H ...
World data centre for microorganisms: an information infrastructure
... data. Utilization of emerging information technology provides the possibility to form such an integrated data platform. Although some culture collections have already developed advanced data platforms, a large number of collections lag behind in digitization, largely because of the lack of facilitie ...
... data. Utilization of emerging information technology provides the possibility to form such an integrated data platform. Although some culture collections have already developed advanced data platforms, a large number of collections lag behind in digitization, largely because of the lack of facilitie ...
IOSR Journal of Computer Engineering (IOSRJCE) ISSN: 2278-0661, ISBN: 2278-8727
... grows every twelve months at a rate of 100%, which shows that a lot of data are obtained and still more data are still being collected. Data mining is a useful tool to tackle the need for sifting useful information such as hidden patterns from databases; especially now the accumulation of data is in ...
... grows every twelve months at a rate of 100%, which shows that a lot of data are obtained and still more data are still being collected. Data mining is a useful tool to tackle the need for sifting useful information such as hidden patterns from databases; especially now the accumulation of data is in ...
Data mining for agent reasoning: A synergy for training intelligent
... knowledge into the agents of the MAS. This way we can easily propose MAS as an add-o3n solution for the enhancement and increase of value of legacy systems. 3. Data miner: a tool for training and retraining agents In order to enable the incorporation of knowledge into agents, we have implemented a t ...
... knowledge into the agents of the MAS. This way we can easily propose MAS as an add-o3n solution for the enhancement and increase of value of legacy systems. 3. Data miner: a tool for training and retraining agents In order to enable the incorporation of knowledge into agents, we have implemented a t ...
SQL: Queries, Constraints, Triggers
... Modeling data warehouses: dimensions & measures Star schema: A fact table in the middle connected to a set of dimension tables Snowflake schema: A refinement of star schema where some dimensional hierarchy is normalized into a set of smaller dimension tables, forming a shape similar to snowflake ...
... Modeling data warehouses: dimensions & measures Star schema: A fact table in the middle connected to a set of dimension tables Snowflake schema: A refinement of star schema where some dimensional hierarchy is normalized into a set of smaller dimension tables, forming a shape similar to snowflake ...
Integrating Data Mining with SQL Databases
... OLE DB definition: cases and models. • Input data is in the form of a set of cases (caseset). A case captures the traditional view of an “observation” by machine learning algorithms as consisting of all information known about a basic entity being analyzed for mining. Of course, structurally, a set ...
... OLE DB definition: cases and models. • Input data is in the form of a set of cases (caseset). A case captures the traditional view of an “observation” by machine learning algorithms as consisting of all information known about a basic entity being analyzed for mining. Of course, structurally, a set ...
process and application of data mining in internet based learning in
... relationships among a large amount of data. A in EDM was presented, from applying data mining for understanding student retention and attrition to finding new ways of making individual student. Many opportunities be existent to study EDM from an organizational unit of analysis to individual course-l ...
... relationships among a large amount of data. A in EDM was presented, from applying data mining for understanding student retention and attrition to finding new ways of making individual student. Many opportunities be existent to study EDM from an organizational unit of analysis to individual course-l ...
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.