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## Data Preprocessing

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**Data Preprocessing**Peter Brezany Institut für Softwarewissenschaften Universität Wien Tel. 4277 38825 Sprechstunde: Di, 12.30-13.30**Today‘s real-world databases are highly susceptible to**noisy, missing, and inconsistent data. „How can the data be preprocessed in order to help improve the quality of the data and, consequently, of the mining results?“ There are a number of data preprocessing techniques: Data cleaning can be applied to remove noise (missing values, or containing errors, or outlier values that deviate from the expected) and correct inconsistencies in the data. Data integration merges data from multiple sources into a coherent data source, such as a data warehouse or a data cube. Data transformation, such as normalization, may be applied to improve the accuracy and efficiency of mining algorithms. Data reduction can reduce the data size by aggregating, eliminating redundant features, or clustering, for instance. These data preprocessing techniques, when applied prior to mining, can substantially improve the overall quality of the patterns mined and/or the time required for the actual mining. Introduction**Forms of Data Preprocessing**Fig. 3.1**Real-world data tend to be incomplete, noisy, and**inconsistent. Data cleaning routines attempt to fill in missing values, smooth out noise while identifying outliers, and correct inconsistencies in the data. This lecture is going to introduce basic methods for data cleaning. Data Cleaning**Imagine that you need to analyze AllElectronics sales and**customer data. You note that many tuples have no recorded value for several attributes, such as customer income. Possible solutions: (1) Ignore the tuple: not very effective, unless many attributes with missing values. Especially poor when the percentage of missing values per attribute varies considerably. (2) Fill in the missing value manually: time-consuming and not feasible gven a large data set with many missing values. (3) Use a global constant to fill in the missing value: Replace all missing attribute values by the same constant, such as a label like “Unknown“ or . If missing values are replaced by, say, Unknown“, then the mining program may mistakenly think that they form an interesting concept. Hence, although his method is simple, it is not recommended. (4) Use the attribute mean to fill in the missing value: For example, suppose that the average income of AllElectronics customer is $28,000. Use this value to replace the missing value for income. Missing Values**(5) Use the attribute mean for all samples belonging to the**same class as the given tuple: For example, if classifying customers according to credit_risk, replace the missing value with the average income value for customers in the same credit risk category as that of the given tuple. (6) Use the most probable value to fill in the missing value: This may be determined with regression, inference-based tools using a Bayesian formalism, or decision tree induction. For example, using the other customer attributes in your data set, you may construct a decision tree to predict the missing values for income. Decision trees are discussed in detail in later lectures. Methods 3 to 6 bias the data. The filled-in value may not be correct. Method 6, however, is a popular strategy; it uses the most information from the present data to predict missing values. By considering the values of other attributes in its estimation of the missing value for income, there is a greater chance that the relationships between income and the other attributes are preserved. Missing Values (2)**„What is noise?“ Noise is a random error or variance in**a measured variable. Given a numeric attribute such as, say, price, how can we „smooth“ out the data to remove the noise? Possible solutions: Binning:Binning methods smooth a sorted data value by consulting its „neighborhood“, that is the values around it. The sorted values are distributed into a number of „buckets,“ or bins (= local smoothing). Sorted data for price (in dollars):4, 8, 15, 21, 21, 24, 25, 28, 34 Partition into (equidepth) bins: Bin 1: 4, 8, 15 Bin 2: 21, 21, 24 Bin 3: 25, 28, 34 Smoothing by bin means: Bin 1: 9, 9, 9 Bin 2: 22, 22, 22 Bin 3: 29, 29, 29 Smothing by bin boundaries: Bin 1: 4, 4, 15 Bin 2: 21, 21, 24 Bin 3: 25, 25, 34 Noisy Data**(2) Clustering: Outliers may be detected by clustering,**where similar values are organized into groups, or „clusters.“ Intuitively, values that fall outside of the set of clusters may be considered outliers. (3) Combined computer and human inspection: Outliers may be identified through a combination of computer and human inspection. (4) Regression: Data can be smoothed by fitting the data to a function, such as with regression. Linear regression involves finding the „best“ line to fit 2 variables, so that one variable can be used to predict the other. Multiple linear regression is an extension of linear regression, where more than 2 variables are involved. Noisy Data (2)**Inconsistent data can defeat any modeling technique until**the inconsistency is discovered. A fundamental problem here is that different things may be represented by the same name in different systems, and the same thing may be represented by different names in different systems. Problems with data consistency also exist when data originates from a single application system. Example: An insurance company offers car insurance. A field identifying „auto_type“ seems innocent enough, but it turns out that the labels entered into the system – „Merc“, „Mercedes“, „M-Benz“,and „Mrcds“ all represent the same manufacturer. Inconsistent Data**Data integration combines data from multiple sources into a**coherent data source, as in data warehousing. These sources may include multiple databases, data cubes, or flat files. There are a number of issues to consider: 1.Schema integration: How can equivalent real-world entities from multiple data sources be matched up? This is referred to as the entity identification problem. E.g., how can the data analyst or the computer be sure that customer_id in one database and cust_number in another refer to the same entity? Solution: metadata, semantics description, ontologies. 2. Redundancy: An attribute may be redundant if it can be „derived“ from another table, such as annual revenue. Inconsistencies in attribute naming can also cause redundancies in the resulting data set. Some redundancies can be detected by correlation analysis. 3. Detection and resolution of data value conflicts (differences in representation, scaling, or encoding): For example, a weight attribute may be stored in metric units in one system and British imperial units in another. The price of different hotels may involve not only diferent currencies but also different services (such as free breakfast) and taxes. Such semantic heterogeneity of data poses great challenges in data integration. Data Integration**The data are transformed or consolidated into forms**appropriate for mining. This process can involve: Smoothing, which works to remove the noise from data (e.g.,binning, clustering, and regression) – a form of data cleaning (already discussed). Aggregation, where summary or aggregation operations are applied to the data a typical step used in constructing a data cube. Generalization of the data, where low-level or „primitive“ (raw) data are replaced by higher-level concepts through the use of concept hierarchies. E.g., categorical (discrete) attributes, like street, can be generalized to higher-level concepts, like city or country. Similarly, values for numeric attributes, like age, may be mapped to higher-level concepts, like young, middle-aged, and senior. Normalization, where the attribute data are scaled so as to fall within a small specified range, such as –1.0 to 1.0, or 0.0 to 1.0. Attribute construction (or feature construction), where new attributes are constructed and added from the given set of attributes to help the mining process. Data Transformation**Complex data analysis and mining on huge amounts of data may**take a very long time, making such analysis impractical or infeasible. Data reduction techniques can be applied to obtain a reduced representation of the data set that is much smaller in volume, yet clesely maintaining the integrity of the original data. Mining on the reduced data set should be more efficient yet produce the same (almost the same) analytical results. Strategies for data reduction include the following: Data cube aggregation, where aggregation operations are applied to the data in the construction of a data cube (already discussed). Dimension reduction, where irrelevant, weakly relevant, or redundant attributes or dimensions may be detected and removed. Data Reduction**3. Data compression, where encoding mechanisms are used to**reduce the data set size (e.g., the discrete wavelet transform). 4. Numerosity reduction, where the data are replaced by alternative, smaller data representations such as parametric models (which need store only the model parameters instead of the actual data – e.g., linear regression), or nonparametric methods such as clustering, sampling, and the use of histograms. 5. Discretization and concept hierarchy generation, where raw data values for attributes are replaced by ranges or higher conceptual levels. Data Reduction (2)**Sampling can be used as a data reduction technique since it**allows a large data set to be represented by a much smaller random sample (or subset) of the data. Suppose that a large data set, D, contains N tuples. Let‘s have a look at some possible samples for D. Simple random sample without replacement (SRSWOR) of sizen: This is created by drawing n of the N tuples from D (n<N), where the probability of drawing any tuple in D is 1/N, that is all tuples are equally likely. Simple random sample with replacement (SRSWR) of sizen: This is smilar to SRSWOR, except that each time a tuple is drawn from D, it is recorded and then replaced. That is, after a tuple is drawn, it is placed back in D so that it can be drawn again. Sampling**Cluster sample: If the tuples in D are grouped into M**mutually disjoint „clusters“. then an SRS of m clusters can be obtained, where m<M. E.g., tuples in a database are usually retrieved a page at a time, so that each page can be considered a cluster. A reduced data representation can be obtained by applying, say SRSWOR to the pages, resulting in a cluster sample of the tuples. Stratified sample: If D is divided into mutually disjoint parts called strata, a stratified sample of D is generated by obtaining an SRS at each stratum. E.g., a stratified sample may be obtained from customer data, where a stratum is created for each customer age group. In this way, the age group having the smallest number of customers will be sure to be represented. The above samples are illustrated in the figure in the next slide. An advantage of sampling for data reduction is that the cost of obtaining a sample is proportional to the size of the sample, n, as opposed to N, the data set size. Hence, sampling complexity is potentially sublinear to the size of the data. Sampling (2)