Page 20 - Demo

%u062c%u0645%u064a%u0639 %u0627%u0644%u062d%u0642%u0648%u0642 %u0645%u062d%u0641%u0648%u0638%u0629 %u0640 %u0627%u0625%u0644%u0639%u062a%u062f%u0627%u0621 %u0639%u0649%u0644 %u062d%u0642 %u0627%u0645%u0644%u0624%u0644%u0641 %u0628%u0627%u0644%u0646%u0633%u062e %u0623%u0648 %u0627%u0644%u0637%u0628%u0627%u0639%u0629 %u064a%u0639%u0631%u0636 %u0641%u0627%u0639%u0644%u0647 %u0644%u0644%u0645%u0633%u0627%u0626%u0644%u0629 %u0627%u0644%u0642%u0627%u0646%u0648%u0646%u064a%u0629202. Sequence Data: Sequence data consists of a data set that is a sequence of individual entities, such as a sequence of words or letters. It is quite like sequential data, except that there are no time stamps; instead, there are positions in an ordered sequence. For example, the genetic information of plants and animals can be represented in the form of sequences of nucleotides that are known as genes 3. Time Series Data: Time series data is a special type of sequential data in which each record is a time series, a series of measurements taken over time. For example, a financial data set might contain objects that are time series of the daily prices of various stocks 4. Spatial Data: Some objects have spatial attributes, such as positions or areas, as well as other types of attributes. An example of spatial data is weather data (precipitation, temperature, pressure) that is collected for a variety of geographical locations III. Statistical Descriptions of Data For data preprocessing to be successful, it is essential to have an overall picture of your data. Basic statistical descriptions can be used to identify properties of the data and highlight which data values should be treated as noise or outliers. 1- Measuring the Central Tendency: Central tendency measures the location of the middle or center of a data distribution. We measure central tendency by: a) The mean: This is called the weighted arithmetic mean or the weighted average. It is calculated as: 1 nx = xin i=1n is sample size and N is population size.