Data Mining: Exploring Data,Lecture Notes for Chapter 3Introduction to Data Mining by Tan, Steinbach, Kumar,What is data exploration?,Key motivations of data exploration include Helping to select the right tool for preprocessing or analysis Making use of humans abilities to recognize patternsPeople can recognize patterns not captured by data analysis tools Related to the area of Exploratory Data Analysis (EDA) Created by statistician John Tukey Seminal book is Exploratory Data Analysis by Tukey A nice online introduction can be found in Chapter 1 of the NIST Engineering Statistics Handbook http:/www.itl.nist.gov/div898/handbook/index.htm,A preliminary exploration of the data to better understand its characteristics.,Techniques Used In Data Exploration,In EDA, as originally defined by Tukey The focus was on visualization Clustering and anomaly detection were viewed as exploratory techniques In data mining, clustering and anomaly detection are major areas of interest, and not thought of as just exploratoryIn our discussion of data exploration, we focus on Summary statistics Visualization Online Analytical Processing (OLAP),Iris Sample Data Set,Many of the exploratory data techniques are illustrated with the Iris Plant data set. Can be obtained from the UCI Machine Learning Repository http:/www.ics.uci.edu/mlearn/MLRepository.html From the statistician Douglas Fisher Three flower types (classes):SetosaVirginica Versicolour Four (non-class) attributesSepal width and lengthPetal width and length,Virginica. Robert H. Mohlenbrock. USDA NRCS. 1995. Northeast wetland flora: Field office guide to plant species. Northeast National Technical Center, Chester, PA. Courtesy of USDA NRCS Wetland Science Institute.,Summary Statistics,Summary statistics are numbers that summarize properties of the dataSummarized properties include frequency, location and spreadExamples: location - mean spread - standard deviationMost summary statistics can be calculated in a single pass through the data,Frequency and Mode,The frequency of an attribute value is the percentage of time the value occurs in the data set For example, given the attribute gender and a representative population of people, the gender female occurs about 50% of the time. The mode of a an attribute is the most frequent attribute value The notions of frequency and mode are typically used with categorical data,Percentiles,For continuous data, the notion of a percentile is more useful. Given an ordinal or continuous attribute x and a number p between 0 and 100, the pth percentile is a value of x such that p% of the observed values of x are less than . For instance, the 50th percentile is the value such that 50% of all values of x are less than .,Measures of Location: Mean and Median,The mean is the most common measure of the location of a set of points. However, the mean is very sensitive to outliers. Thus, the median or a trimmed mean is also commonly used.,Measures of Spread: Range and Variance,Range is the difference between the max and min The variance or standard deviation is the most common measure of the spread of a set of points. However, this is also sensitive to outliers, so that other measures are often used.,Visualization,Visualization is the conversion of data into a visual or tabular format so that the characteristics of the data and the relationships among data items or attributes can be analyzed or reported.Visualization of data is one of the most powerful and appealing techniques for data exploration. Humans have a well developed ability to analyze large amounts of information that is presented visually Can detect general patterns and trends Can detect outliers and unusual patterns,Example: Sea Surface Temperature,The following shows the Sea Surface Temperature (SST) for July 1982 Tens of thousands of data points are summarized in a single figure,Representation,Is the mapping of information to a visual format Data objects, their attributes, and the relationships among data objects are translated into graphical elements such as points, lines, shapes, and colors. Example: Objects are often represented as points Their attribute values can be represented as the position of the points or the characteristics of the points, e.g., color, size, and shape If position is used, then the relationships of points, i.e., whether they form groups or a point is an outlier, is easily perceived.,Arrangement,Is the placement of visual elements within a display Can make a large difference in how easy it is to understand the data Example:,Selection,Is the elimination or the de-emphasis of certain objects and attributes Selection may involve the chossing a subset of attributes Dimensionality reduction is often used to reduce the number of dimensions to two or three Alternatively, pairs of attributes can be considered Selection may also involve choosing a subset of objectsA region of the screen can only show so many points Can sample, but want to preserve points in sparse areas,