Seventh Semester Information Technology Subject Code: BTIT701T

1. Why Preprocess the Data?

Real-world data is often messy, incomplete, inconsistent, and contains noise. Before raw data can be effectively used for data mining tasks like classification, clustering, or association rule mining, it needs to be prepared and refined. This crucial preparatory step is known as Data Preprocessing. Without it, the results of any data mining effort could be inaccurate, misleading, or simply impossible to obtain. The principle of “Garbage In, Garbage Out” (GIGO) strongly applies in data mining; the quality of the input data directly determines the quality of the output insights [13][14].

There are several compelling reasons why data preprocessing is essential:

• Improving Data Quality: Raw data often suffers from various quality issues. It might contain missing values (due to unrecorded entries or data loss), noisy data (containing errors, outliers, or meaningless values), and inconsistencies (discrepancies in codes, names, or formats). Preprocessing techniques like data cleaning help address these issues, leading to a more accurate and reliable dataset [13][15].
• Enhancing Mining Efficiency and Performance: Processing large volumes of raw, unprepared data can be computationally expensive and time-consuming. Data reduction techniques (like dimensionality reduction or numerosity reduction) can significantly reduce the size of the dataset without losing critical information, making the subsequent mining process faster and more efficient [13][16]. Similarly, transforming data into appropriate formats (e.g., normalization) can improve the performance and convergence of certain mining algorithms.
• Ensuring Suitability for Mining Algorithms: Many data mining algorithms have specific requirements regarding the format and type of input data. For example, some algorithms cannot handle missing values directly, while others perform better when numerical attributes are scaled to a specific range (e.g., 0 to 1). Data transformation and discretization techniques ensure the data is in a suitable format for the chosen algorithms [13][17].
• Handling Data Integration Issues: Data for mining often comes from multiple heterogeneous sources (databases, files, web logs). Integrating this data requires resolving issues like schema differences, data format variations, naming conflicts, and identifying redundant or correlated attributes. Data integration preprocessing steps are necessary to create a unified and consistent dataset [13].
• Extracting Meaningful Patterns: The ultimate goal of data mining is to extract meaningful patterns and insights. Preprocessing helps to highlight the underlying structure of the data by removing noise and irrelevant information, making it easier for mining algorithms to discover significant patterns [14].

In summary, data preprocessing is not an optional step but a fundamental requirement for successful data mining. It transforms raw, often unusable data into a clean, consistent, and well-structured format, thereby improving the accuracy, efficiency, and reliability of the entire data mining process and the insights derived from it [15].

2. Major Tasks in Data Preprocessing

Data preprocessing encompasses a wide range of techniques aimed at transforming raw data into a clean and usable format. While the specific steps can vary depending on the data and the mining task, the major tasks generally fall into four key categories [13]:

1. Data Cleaning: This task focuses on identifying and correcting errors, inconsistencies, and inaccuracies in the data. The goal is to improve the overall quality and reliability of the dataset. Common data cleaning activities include:
   • Handling Missing Values: Identifying entries where data is missing and deciding how to address them (e.g., ignoring the record, filling the gap manually, using statistical imputation like mean/median/mode, or using more sophisticated prediction methods).
   • Smoothing Noisy Data: Identifying and correcting errors or outliers in the data. Techniques include binning (grouping values and smoothing by bin means or boundaries), regression (fitting data to a function), and clustering (detecting outliers that fall outside clusters).
   • Resolving Inconsistencies: Correcting discrepancies in codes, names, formats, or units across different data sources or within the same dataset.
   • Removing Duplicates: Identifying and eliminating redundant records.
2. Data Integration: This task involves combining data from multiple, often heterogeneous, sources into a coherent data store, like a data warehouse or a unified dataset for analysis. Key challenges and activities include:
   • Schema Integration: Merging database schemas from different sources, which may have different structures and naming conventions.
   • Entity Identification: Identifying real-world entities that are represented differently across various data sources (e.g., matching customer records with slightly different names or addresses).
   • Handling Data Redundancy and Correlation: Detecting and resolving situations where the same information is stored in multiple places (redundancy) or where attributes are highly correlated (one attribute can be derived from another). Redundancy can sometimes be removed to save space and improve efficiency.
   • Resolving Data Value Conflicts: Addressing inconsistencies where the same real-world entity has conflicting attribute values in different sources.
3. Data Reduction: Since data mining often deals with massive datasets, data reduction techniques are employed to obtain a reduced representation of the dataset volume that is much smaller in size yet produces the same (or almost the same) analytical results. Strategies include:
   • Dimensionality Reduction: Reducing the number of attributes (dimensions) under consideration. This can involve techniques like feature selection (choosing a subset of relevant attributes) or feature extraction/transformation (creating new, fewer attributes that capture the essence of the original ones, e.g., Principal Component Analysis – PCA).
   • Numerosity Reduction: Replacing the original data volume with alternative, smaller forms of data representation. This includes parametric methods (like regression models) and non-parametric methods (like histograms, clustering, sampling).
   • Data Compression: Using encoding mechanisms (lossy or lossless) to reduce the storage space required for the data.
4. Data Transformation and Data Discretization: Data is transformed or consolidated into forms appropriate for mining. Strategies include:
   • Normalization: Scaling attribute data so that it falls within a smaller, specified range, such as 0.0 to 1.0 (min-max normalization) or scaling based on mean and standard deviation (z-score normalization). This prevents attributes with larger ranges from dominating those with smaller ranges.
   • Attribute/Feature Construction: Creating new attributes (features) from the given set of attributes to help the mining process.
   • Aggregation: Summarizing data, such as collecting daily sales figures to compute monthly or yearly totals.
   • Discretization: Replacing raw values of a continuous attribute with interval labels or conceptual labels. This is particularly useful for algorithms that work better with categorical data. Techniques include binning, histogram analysis, and clustering-based discretization.
   • Concept Hierarchy Generation: Organizing attributes into hierarchies (e.g., street < city < state < country) to allow mining at multiple levels of abstraction.

These major tasks are often iterative and may need to be applied multiple times or in different sequences depending on the specific data challenges and the goals of the data mining project.

3. Descriptive Data Summarization

Before diving deep into complex data mining techniques, it’s crucial to get a basic understanding of the data itself. Descriptive Data Summarization involves techniques that help characterize the general properties of the data in the dataset. These summaries provide concise information about the data’s main features, helping analysts identify patterns, spot potential issues like outliers, and get a feel for the data’s distribution [18][19].

Descriptive summaries are often the first step in data exploration and preprocessing. They typically focus on two main aspects: measures of central tendency and measures of data dispersion (spread).

1. Measuring the Central Tendency:
These measures provide a single value that attempts to describe the center or typical value of a distribution for a given attribute.

• Mean (Average): The most common measure, calculated by summing all values and dividing by the number of values. It’s sensitive to outliers (extreme values). For a set of values {x1, x2, …, xN}, the mean (μ) is (Σxi) / N [18].
• Median: The middle value in a dataset that has been sorted in ascending order. If the dataset has an even number of observations, the median is the average of the two middle values. The median is less sensitive to outliers than the mean [18].
• Mode: The value that appears most frequently in a dataset. A dataset can have one mode (unimodal), two modes (bimodal), or multiple modes (multimodal). It’s the only measure of central tendency suitable for categorical data [18].
• Midrange: The average of the maximum and minimum values in the dataset. It’s simple to compute but extremely sensitive to outliers.

2. Measuring the Dispersion (Spread) of Data:
These measures describe how spread out or varied the data points are for a given attribute.

• Range: The difference between the maximum and minimum values in the dataset. It’s simple but sensitive to outliers [19].
• Quartiles and Interquartile Range (IQR): Quartiles divide the sorted data into four equal parts.
  • Q1 (First Quartile): The value below which 25% of the data falls.
  • Q2 (Second Quartile): The median value (50th percentile).
  • Q3 (Third Quartile): The value below which 75% of the data falls.
  • Interquartile Range (IQR): The difference between Q3 and Q1 (IQR = Q3 – Q1). The IQR represents the range covered by the middle 50% of the data and is robust to outliers [19].
• Variance: Measures the average squared deviation of each data point from the mean. A higher variance indicates greater spread.
• Standard Deviation (σ): The square root of the variance. It provides a measure of dispersion in the original units of the data and is widely used. Like the mean, it is sensitive to outliers [19].
• Five-Number Summary: A concise summary consisting of the Minimum, Q1, Median (Q2), Q3, and Maximum values. This summary provides insights into the center, spread, and shape (skewness) of the distribution.

3. Graphical Summaries (Data Visualization):
Visual methods are powerful tools for descriptive data summarization.

• Boxplots (Box-and-Whisker Plots): Visual representation of the five-number summary, useful for comparing distributions and identifying potential outliers.
• Histograms: Bar charts showing the frequency distribution of a continuous attribute by dividing the data range into bins.
• Quantile Plots: Graphs the data values against their rank or quantile, helping to assess the overall distribution.
• Scatter Plots: Used to visualize the relationship between two numerical attributes.

Descriptive data summarization provides essential preliminary insights into the data’s characteristics, guiding subsequent preprocessing steps like data cleaning and transformation, and helping to select appropriate data mining techniques.

4. Data Cleaning

Data cleaning is a critical step in data preprocessing focused on detecting and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset. It directly addresses the quality issues inherent in real-world data, ensuring that subsequent analysis is based on reliable information. The primary goals are to handle missing data and smooth out noisy data [13][20].

1. Handling Missing Values:
Missing data occurs when no data value is stored for a variable in an observation. This can happen due to various reasons like equipment malfunction, data not being entered, misunderstanding, or data being irrelevant to certain cases. Common approaches to handle missing values include:

• Ignoring the Tuple (Listwise Deletion): This is usually done when the class label is missing (for classification tasks) or if the percentage of missing values in a tuple or attribute is very high. However, this method can be ineffective if the missing data is not randomly distributed or if the percentage of missing values is significant across many tuples, as it reduces the sample size [20][21].
• Manual Filling: In some cases, especially with small datasets or critical missing values, domain experts might fill in the missing values manually. This is time-consuming and often infeasible for large datasets.
• Using a Global Constant: Replace all missing attribute values with a single global constant (e.g., “Unknown”, “N/A”, or -1). This is simple but can lead to misleading patterns if the constant is misinterpreted by mining algorithms [13].
• Using Attribute Mean/Median/Mode: Replace missing values with the mean (for symmetric numerical data), median (for skewed numerical data), or mode (for categorical data) of the respective attribute. This is a popular and simple strategy but can reduce the variance of the dataset [20][21].
• Using Attribute Mean/Median/Mode for the Same Class: If dealing with classification, replace the missing value with the mean/median/mode of the attribute for samples belonging to the same class as the tuple with the missing value.
• Using Predictive Models (Regression/Classification): Develop a model (e.g., regression, decision tree, Bayesian classifier) to predict the most probable value for the missing entry based on other attributes in the record. This is generally more sophisticated and often provides more accurate results than simple imputation methods [21].

2. Handling Noisy Data:
Noisy data refers to random errors or variance in a measured variable. It can result from faulty data collection instruments, data entry problems, data transmission issues, or inconsistencies in naming conventions. Techniques to handle noisy data include:

• Binning: This method smooths sorted data values by consulting their “neighborhood” (values around them). The sorted values are distributed into a number of ‘bins’ or ‘buckets’. Then, various methods can be applied to smooth the data within each bin:
  • Smoothing by bin means: Replace each value in a bin with the bin’s mean.
  • Smoothing by bin medians: Replace each value in a bin with the bin’s median.
  • Smoothing by bin boundaries: Replace each value in a bin with the closest boundary value (minimum or maximum value of the bin) [13].
• Regression: Fit data values to a regression function (linear or multiple). The regression function can then be used to predict and smooth the data values, effectively removing noise [13].
• Outlier Analysis (Clustering): Outliers may be identified using clustering analysis, where values that fall outside the set of clusters can be considered outliers or noise. These can then be investigated and potentially removed or corrected [13][22].
• Manual Inspection: Sometimes, visual inspection or domain knowledge can help identify and correct obvious errors or noise, though this is impractical for large datasets.

Effective data cleaning requires careful consideration of the type of data, the extent of the problem (missing values, noise), and the potential impact of different cleaning methods on the final analysis. It’s often an iterative process involving multiple techniques.

5. Data Integration

Data integration is the process of merging data from multiple disparate sources into a single, unified, and consistent dataset [23]. In the context of data mining or warehousing, these sources can include multiple databases, data cubes, flat files, web sources, etc. Effective integration is crucial for providing a comprehensive view for analysis, but it presents several significant challenges:

Challenges in Data Integration:

1. Schema Integration and Object Matching (Entity Identification Problem): Different sources may represent the same real-world concepts using different schemas, attribute names, or data structures. For example, one database might use CustomerID while another uses Cust_ID for the same entity. Integrating these requires matching semantically equivalent entities and attributes from different sources. This is a complex task, often requiring domain knowledge and sophisticated matching algorithms [23][24]. How can we be sure that customer_id in one database and cust_number in another refer to the same entity?
2. Redundancy and Correlation Analysis: Data redundancy occurs when the same information is stored multiple times, either within the same dataset or across different sources. This can happen if attributes are derived from others (e.g., age derived from date of birth) or if data is duplicated across tables. Redundant attributes can skew results and increase storage/processing needs. Identifying and handling redundancy is important. This often involves correlation analysis (for numerical data) or covariance analysis to measure how strongly one attribute implies another. Highly correlated attributes might indicate redundancy [23][25]. For categorical data, techniques like the chi-square test can check for correlation.
3. Data Value Conflict Detection and Resolution: The same real-world entity might have conflicting attribute values in different sources. For example, the address for the same customer might be different in the sales database versus the shipping database, or different units might be used (e.g., metric vs. imperial). Resolving these conflicts requires establishing data quality rules, defining data precedence (which source is more reliable?), or using data reconciliation techniques [24][25].
4. Handling Data Silos: Different departments or systems often operate independently, creating data silos with inconsistent formats and definitions. Integrating these requires breaking down organizational barriers and establishing common data standards [24].
5. Volume and Complexity: Integrating large volumes of data from complex sources requires scalable infrastructure and efficient algorithms [25].

Approaches to Data Integration:

• Tight Coupling (Data Warehousing Approach): Data is extracted from various sources, transformed (cleaned, standardized), and loaded into a central data warehouse (ETL). This provides a unified, consistent view but requires significant upfront effort to build and maintain the warehouse.
• Loose Coupling (Federated Approach / Virtual Integration): A virtual layer or middleware is created that allows users to query data directly from the source systems without physically moving it. Queries are translated and routed to the appropriate sources. This avoids ETL complexity but can strain operational systems and may face performance issues for complex queries [26].
• Common Data Format / Middleware: Data is transformed into a common format and accessed via middleware, offering a balance between the two extremes.

Careful handling of schema integration, entity identification, redundancy, and value conflicts using appropriate metadata management and correlation analysis is key to successful data integration for data mining.

6. Data Reduction

Data mining often involves analyzing massive datasets, which can contain terabytes or even petabytes of data. Running complex mining algorithms on such large datasets can be computationally expensive and time-consuming. Data reduction techniques aim to obtain a reduced representation of the dataset that is much smaller in volume but still retains the integrity of the original data, producing the same (or nearly the same) analytical results [27][28]. Applying data reduction can significantly improve the efficiency of data mining processes.

Major strategies for data reduction include:

1. Dimensionality Reduction:
   This aims to reduce the number of attributes (dimensions or features) in the dataset. High dimensionality can lead to the “curse of dimensionality,” where data becomes sparse, and distances between points become less meaningful, hindering clustering and classification tasks. Reducing dimensions can also help remove irrelevant or noisy features [27][29]. Techniques include:
   • Feature Selection: Identifying and selecting a subset of the original attributes that are most relevant to the mining task, while discarding irrelevant or redundant ones. Methods include filter methods (evaluating attributes independently), wrapper methods (using the target mining algorithm to evaluate attribute subsets), and embedded methods (feature selection integrated into the algorithm).
   • Feature Extraction (Transformation): Creating a smaller set of new attributes (features) that capture the essential information of the original attributes. These new features are combinations or transformations of the original ones.
     • Principal Component Analysis (PCA): A widely used technique that finds a set of orthogonal axes (principal components) that capture the maximum variance in the data. The data can then be projected onto a smaller number of principal components, reducing dimensionality while retaining most of the original information [27].
     • Linear Discriminant Analysis (LDA): A supervised method used primarily for classification tasks, which finds a lower-dimensional space that maximizes class separability.
     • Attribute Construction: Creating higher-level attributes from lower-level ones (covered also in Data Transformation).
2. Numerosity Reduction:
   This aims to reduce the data volume by choosing alternative, smaller forms of data representation. Instead of working with the entire dataset, we use a smaller representation or model.
   • Parametric Methods: Assume the data fits some model and store only the model parameters instead of the actual data. Regression models (linear, multiple) are common examples. The model parameters replace the data, but this only works if the data follows the assumed model [30].
   • Non-parametric Methods: Do not assume specific data models. Techniques include:
     • Histograms: Divide attribute values into ranges (bins) and store only the frequency count for each bin.
     • Clustering: Group similar data points into clusters and store cluster representations (e.g., centroids and counts) instead of individual points.
     • Sampling: Selecting a representative subset (sample) of the data. Various sampling techniques exist (simple random sampling, stratified sampling, cluster sampling). The mining is performed on the sample, which is much smaller than the original dataset [30].
3. Data Compression:
   This involves applying encoding techniques to reduce the size of the data. Compression can be:
   • Lossless: The original data can be perfectly reconstructed from the compressed data (e.g., run-length encoding, Huffman coding). Dimensionality and numerosity reduction techniques can also be considered forms of lossless or lossy compression depending on the method [27][31].
   • Lossy: Reconstruction yields only an approximation of the original data (e.g., JPEG compression for images, some forms of quantization for numerical data). Lossy compression typically achieves higher reduction ratios but sacrifices some information.

Choosing the appropriate data reduction strategy depends on the specific data characteristics, the mining task, and the acceptable trade-off between data reduction efficiency and potential information loss.

7. Data Transformation

Data transformation is the process of converting data from one format or structure into another, making it more suitable for the data mining process. It involves applying mathematical or logical operations to the data attributes. The goal is to improve the accuracy and efficiency of mining algorithms and ensure data compatibility [32][33]. Key data transformation strategies include:

1. Normalization:
   This technique scales attribute data so that it falls within a smaller, specified range, such as [-1, 1] or [0, 1]. Normalization is particularly useful for algorithms that rely on distance measures (like clustering or k-nearest neighbors) or use gradient descent, as it prevents attributes with larger ranges from disproportionately influencing the results. Common normalization methods include:
   • Min-Max Normalization: Performs a linear transformation on the original data. It maps a value v of an attribute A to v’ in the range [new_min_A, new_max_A] using the formula:
     v’ = (( v – min_A ) / ( max_A – min_A )) * ( new_max_A – new_min_A ) + new_min_A
     where min_A and max_A are the minimum and maximum values of attribute A. If the new range is [0, 1], the formula simplifies to v’ = ( v – min_A ) / ( max_A – min_A ) [34][35]. This preserves relationships among the original data values but is sensitive to outliers (which determine min_A and max_A).
   • Z-Score Normalization (Standardization): Transforms the values based on the mean (μ) and standard deviation (σ) of the attribute A. A value v is mapped to v’ using the formula:
     v’ = ( v – μ ) / σ
     This method results in data with a mean of 0 and a standard deviation of 1. It is less sensitive to outliers than min-max normalization but doesn’t guarantee a fixed range [34][35].
   • Normalization by Decimal Scaling: Moves the decimal point of values of attribute A based on the maximum absolute value of A. A value v is normalized to v’ = v / 10^j, where j is the smallest integer such that max(|v’|) < 1.
2. Attribute Construction (Feature Engineering):
   This involves creating new attributes (features) from the given set of attributes to help improve the accuracy and understanding of the mining process. New attributes can capture interactions or relationships that are not explicit in the original data [32][36]. Examples include:
   • Creating a BMI (Body Mass Index) attribute from height and weight.
   • Creating a profit attribute from revenue and cost.
   • Extracting day_of_week or month from a date attribute.
   • Combining several sparse categorical features into a single, more informative feature.
     Effective feature engineering often requires domain knowledge and creativity.
3. Aggregation:
   This involves summarizing data by combining multiple data points into a single value. It’s often used in constructing data cubes or performing analysis at different levels of granularity. For example, aggregating daily sales data to get monthly or quarterly totals, or calculating average sales per region [13].
4. Smoothing:
   This refers to techniques used to remove noise from the data. Methods like binning, regression, and clustering (as discussed under Data Cleaning) can be considered transformation techniques aimed at smoothing the data.

Data transformation is a powerful tool in preprocessing, enabling analysts to prepare data in a way that maximizes the potential for discovering meaningful patterns during the data mining phase.

8. Data Discretization

Data discretization is a specific type of data transformation that involves converting continuous numerical attributes into discrete or nominal intervals (categories). Many data mining algorithms, particularly certain classification algorithms like decision trees (e.g., ID3) or association rule mining algorithms, are designed to work primarily with categorical data or perform better with it. Discretization helps bridge this gap by transforming continuous data into a finite number of intervals, simplifying the data and potentially improving model performance or interpretability [37][38].

Discretization techniques can be broadly categorized based on whether they use class information (supervised) or not (unsupervised), and whether they create intervals top-down (splitting) or bottom-up (merging).

Common discretization methods include:

1. Binning (Equal-Width or Equal-Frequency):
   This is a simple unsupervised method.
   • Equal-Width Binning: Divides the range of the continuous attribute into k intervals of equal size. The width is calculated as (max_value – min_value) / k. This method is straightforward but can be heavily affected by outliers, potentially resulting in bins with very few data points or bins where most data points are concentrated.
   • Equal-Frequency (or Equal-Depth) Binning: Divides the range into k intervals, each containing approximately the same number of data samples (N/k, where N is the total number of samples). It handles outliers better than equal-width binning but can result in intervals with very different widths [37][39].
     Binning is often used for data smoothing as well, by replacing values with bin means or medians after discretization.
2. Histogram Analysis:
   Similar to binning, this unsupervised method uses histograms to partition the data. A histogram graphically represents the frequency distribution of an attribute. The algorithm analyzes the histogram to identify clusters or natural breaks in the data distribution, which are then used to define the interval boundaries [37][40]. Different histogram types (e.g., equal-width, equal-frequency) can be used as a basis.
3. Clustering Analysis:
   Unsupervised clustering algorithms (like K-Means) can be applied to the continuous attribute values. The data points are grouped into clusters, and the intervals for discretization are determined based on these clusters. For example, all values within a cluster might be mapped to a single discrete category [38][40]. This approach can capture natural groupings in the data.
4. Decision Tree-Based (Entropy-Based) Discretization:
   This is a supervised method that uses class information. A decision tree algorithm (like C4.5 or CART) is run on the continuous attribute, using the class labels as the target. The split points identified by the decision tree algorithm (which aim to maximize information gain or minimize impurity) are used as the interval boundaries for discretization [40]. This method directly optimizes the intervals for the classification task.
5. Correlation Analysis (ChiMerge):
   ChiMerge is a supervised, bottom-up discretization method. It starts by placing each distinct value of the continuous attribute into its own interval. It then iteratively merges adjacent intervals based on a chi-square (χ²) test, which assesses the statistical dependence between the attribute intervals and the class labels. Merging continues until a stopping criterion (e.g., a significance threshold or a maximum number of intervals) is met. This method aims to find intervals that have a strong correlation with the class labels.

Concept Hierarchy Generation:
Discretization is often followed by generating concept hierarchies for the newly created discrete attributes. For example, numerical age ranges (0-10, 11-20, …) can be mapped to conceptual labels (child, youth, adult, senior). This allows for mining at multiple levels of abstraction.

The choice of discretization method depends on factors like the data distribution, the presence of class labels (supervised vs. unsupervised), and the requirements of the subsequent data mining algorithm.

References:

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Question Bank:

Unit II: Data Preprocessing – Question Bank

Instructions: Answer the following questions comprehensively. Marks may vary based on the depth and accuracy of the answer.

1. Explain why data preprocessing is a crucial step in the data mining process. Discuss at least three major reasons with examples, highlighting the potential consequences of mining raw, unprepared data.
2. Outline and briefly describe the four major tasks involved in data preprocessing: Data Cleaning, Data Integration, Data Reduction, and Data Transformation/Discretization.
3. What is Descriptive Data Summarization? Explain the difference between measures of central tendency (provide examples like mean, median, mode) and measures of data dispersion (provide examples like range, IQR, standard deviation). When might the median be preferred over the mean?
4. Discuss the common techniques for handling missing values in data cleaning. Compare and contrast at least three different methods (e.g., tuple deletion, mean/median imputation, predictive modeling), outlining their respective advantages and disadvantages.
5. What is noisy data? Describe two different techniques used to handle noisy data during data cleaning (e.g., binning, regression, clustering/outlier analysis).
6. Explain the main challenges encountered during Data Integration. Focus specifically on the Entity Identification Problem and Data Redundancy. How can correlation analysis help in identifying redundancy?
7. What is the primary goal of Data Reduction? Describe the difference between Dimensionality Reduction and Numerosity Reduction, providing one example technique for each (e.g., PCA for dimensionality, sampling for numerosity).
8. Explain the purpose of Data Transformation. Describe the Min-Max Normalization and Z-Score Normalization techniques. Provide the formula for each and discuss a scenario where one might be preferred over the other.
9. What is Data Discretization? Why is it sometimes necessary or beneficial? Describe two different unsupervised discretization techniques (e.g., equal-width binning, equal-frequency binning, histogram analysis, clustering).
10. Compare and contrast supervised versus unsupervised discretization methods. Give an example of a supervised method (e.g., decision tree-based) and explain how it utilizes class information.