C-TREND: Temporal Cluster Graphs for Identifying and Visualizing Trends in Multi-attribute Transactional Data

Organizations and firms are capturing increasingly more data about their customers, suppliers, competitors, and business environment. Most of this data is multi-attribute (multidimensional) and temporal in nature. Data mining and business intelligence techniques are often used to discover patterns in such data; however, mining temporal relationships typically is a complex task. We propose a new data analysis and visualization technique ....etc

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Abstract Test card for color TV is a widely used method to evaluate the quality of a TV set directly. We have made a test signal generator (which can also generate synchronizing (abbreviated to sync hereafter) signals) mainly based on microprocessor Z-80. Taking the advantage of microprocessor programming, combining with the technique of microprocessor, digital circuits and analogue devices, we use software as far as possible to generate signals directly or to simplify the controlling circuits. Those signals, such as composite sync signal, bla ....etc

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to get information about the topic C-TREND:Temporal Cluster Graphs for Identifying and Visualizing Trends full report ,ppt and related topic refer the page link bellow

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Signal flow graph an additional technique that is very useful for the analysis of microwave networks in terms of transmitted and reflected waves
Consider the flow graph of an arbitrary two-port network
The flow graph gives an intuitive graphical illustration of the network behavior
A wave of amplitude a1 incident at port 1 is split:
1) part going throughS11 and out port 1 as a reflected wave
2) part transmitted through S21 to node b2.
3) At node b2:
- the wave goes out port 2;
- if a load with non ze ....etc

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C-TREND:Temporal Cluster Graphs for Identifying and Visualizing Trends in Multi-attribute Transactional Data

Organizations and firms are capturing increasingly more data about their customers, suppliers, competitors, and business environment. Most of this data is multi-attribute (multidimensional) and temporal in nature. Data mining and business intelligence techniques are often used to discover patterns in such data; however, mining temporal relationships typically is a complex task. We propose a new data analysis and visualization techniqu ....etc

[:=Read Full Message Here=:]