Exercises 47378 Consider The Traffic Accident Data

78 Exercises 47378 Exercises1 Consider The Traffic Accident Data S

Consider the traffic accident data set shown in Table 7.10. Show a binarized version of the data set. What is the maximum width of each transaction in the binarized data? Assuming that support threshold is 30%, how many candidate and frequent itemsets will be generated? Create a data set that contains only the following asymmetric binary attributes: (Leather: Bad, Driver's condition: Alcohol-impaired, Traffic violation: Yes, Seat belt: No, Crash severity: Major). For Traffic violation, only None has a value of 0. The rest of the attribute values are assigned to 1. Assuming that support threshold is 30%, how many candidate and frequent itemsets will be generated? Compare the number of candidate and frequent itemsets generated in parts (c) and (d).

Consider the data set shown in Table 7.11. Suppose we apply the following discretization strategies to the continuous attributes of the data set.

i. Construct a binarized version of the data set. ii. Derive all the frequent itemsets having support > 30%. For each strategy, answer the following questions:

• Construct a binarized version of the data set.
• Derive all the frequent itemsets having support > 30%.

The continuous attribute can also be discretized using a clustering approach.

i. Plot a graph of temperature versus pressure for the data points shown in Table 7.11. ii. How many natural clusters do you observe from the graph? Assign a label (C1, C2, etc.) to each cluster in the graph. iii. What type of clustering algorithm do you think can be used to identify the clusters? State your reasons clearly. iv. Replace the temperature and pressure attributes in Table 7.11 with asymmetric binary attributes C1, C2, etc. Construct a transaction matrix using the new attributes (along with attributes Alarm1, Alarm2, and Alarm3). v. Derive all the frequent itemsets having support > 30% from the binarized data. Consider the data set shown in Table 7.12. The first attribute is continuous, while the remaining two attributes are asymmetric binary. A rule is considered to be strong if its support exceeds 15% and its confidence exceeds 60%. The data given in Table 7.12 supports the following two strong rules: (i) {(1

(a) Compute the support and confidence for both rules. (b) To find the rules using the traditional Apriori algorithm, we need to discretize the continuous attribute A. Suppose we apply the equal width, ...

Paper For Above instruction

Analyzing Traffic Accident Data and Applying Discretization Strategies for Data Mining

In contemporary data mining applications, the analysis of traffic accident data offers profound insights into safety patterns and risk factors. Tackling this data involves multiple preprocessing steps such as data binarization, discretization, and clustering, which enhance the efficiency of frequent pattern mining. This paper discusses methodologies for transforming continuous and categorical data into suitable formats for association rule mining, illustrating how these transformations influence the number of candidate and frequent itemsets generated, as well as the overall interpretability of the extracted knowledge.

Data Binarization and Frequent Itemsets

The initial step involves binarizing the traffic accident dataset as shown in Table 7.10. This entails converting each categorical attribute (such as weather, driver condition, traffic violation, seat belt use, and severity) into binary variables indicating the presence or absence of specific conditions. For example, the variable "Weather: Good" becomes a binary feature, with 1 indicating good weather. With this representation, the maximum transaction width equates to the number of binary attributes created. If the support threshold is set at 30%, the Apriori algorithm would generate candidate itemsets by considering all combinations of these attributes. For instance, with n binary features, the potential candidate itemsets are 2^n - 1, and the actual number of frequent itemsets depends on the data's support distribution.

In a separate scenario, a refined dataset is constructed with asymmetric binary attributes such as Leather, Driver's condition, Traffic violation, Seat belt, and Crash severity, where only "None" for Traffic violation is assigned 0, while all other conditions are 1. Given a 30% support threshold, the size of candidate and frequent itemsets can be calculated similarly, but the attributes' specific structure often results in fewer candidate combinations, thereby influencing computational efficiency. Comparing the total candidate and frequent itemsets in parts (c) and (d) reveals the impact of attribute encoding strategies on the mining process.

Discretization Strategies and Clustering

When continuous data attributes like temperature and pressure are involved, discretization becomes essential. The first strategy divides the range into three equal-sized bins, producing a binarized dataset where each continuous attribute is represented by multiple binary features corresponding to its bin membership. Alternatively, a data-driven approach uses equal frequency bins, ensuring each bin has roughly the same number of transactions, leading to potentially more balanced groupings. These transformations enable the application of association rule mining on numeric data.

Graphical analysis of the temperature versus pressure plot often reveals natural clusters corresponding to operating conditions or fault states. Clustering algorithms such as K-means or hierarchical clustering can identify these groupings. The choice depends on factors like cluster shape and data distribution; for linear, well-separated groups, K-means is effective. Once identified, each cluster is labeled (C1, C2, etc.), and the original continuous attributes are replaced with cluster labels, which are then binarized for frequent pattern mining.

Rule Support and Confidence Computation

Using the dataset shown in Table 7.12, support and confidence of candidate rules are computed based on their occurrence frequencies. For the two given rules, the support is determined by counting transactions supporting both antecedent and consequent, while confidence measures the proportion of antecedent-supporting transactions that also support the consequent. For example, if rule (i) appears in 20 out of 100 transactions, support is 20%, and confidence is calculated as the support divided by the total transactions supporting the antecedent.

Discretizing a continuous attribute like A is crucial for the Apriori algorithm. Equal width slicing partitions A into ranges with equal spans, while equal frequency splits aim for bins with similar transaction counts. Both approaches influence the number of candidate itemsets and the rules' strength metrics, impacting the quality and interpretability of the derived rules. Ultimately, thoughtful discretization enhances the meaningfulness of the association rules in the context of the domain.

Conclusion

This comprehensive analysis demonstrates the importance of data transformation techniques in association rule mining. Proper binarization, discretization, and clustering enable effective extraction of insightful patterns from complex datasets such as traffic accidents. Future work may explore advanced clustering algorithms and adaptive discretization methods to further optimize pattern discovery process, thereby supporting data-driven decision-making in traffic safety management.

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