Choose The Area Of Your Preference Whatever You Would Like ✓ Solved

Choose The Area Of Your Preference Whatever You Would Like

Choose the area of your preference, whatever you would like to describe in a dataset and explain using data mining. Create a data file in .arff format containing about 20 entries, each described by about 4 attributes, with the last attribute containing your preference (class attribute). Compare 3 algorithms for classification of your data: decision trees, a classification or an association rule learner, and naive Bayes. For each algorithm check what the error is and observe the generated rules.

Paper For Above Instructions

Data mining has become an essential tool in analyzing large datasets across various domains. This paper will explore the area of “Movies” as a preference dataset, analyzing movie features and their correlation with user preferences using data mining techniques. We will create a dataset in the ARFF format, consisting of movie attributes and the user's preference regarding each film. Subsequently, we will compare three classification algorithms: Decision Trees, Naive Bayes, and Association Rule Learners, to understand which algorithm predicts movie preferences most effectively and analyze the rules generated by these algorithms.

Dataset Creation

The following is the dataset created in ARFF format:

@relation movies


@attribute title string


@attribute genre {Action, Comedy, Drama, Horror, Romance}


@attribute rating numeric


@attribute year numeric


@attribute like_it {yes, no}


@data


"Avengers: Endgame", Action, 8.4, 2019, yes


"The Godfather", Drama, 9.2, 1972, yes


"Joker", Drama, 8.5, 2019, yes


"Get Out", Horror, 7.7, 2017, yes


"Parasite", Comedy, 8.6, 2019, yes


"Toy Story", Animation, 8.3, 1995, yes


"Trainspotting", Drama, 8.1, 1996, yes


"Inception", Action, 8.8, 2010, yes


"Frozen", Animation, 7.4, 2013, no


"Twilight", Romance, 5.2, 2008, no


"Step Brothers", Comedy, 6.9, 2008, yes


"Blade Runner 2049", Sci-Fi, 8.0, 2017, yes


"Schindler's List", Drama, 9.0, 1993, yes


"It", Horror, 7.3, 2017, no


"Deadpool", Action, 8.0, 2016, yes


"The Notebook", Romance, 7.8, 2004, yes


"Bridesmaids", Comedy, 6.8, 2011, no


"Zodiac", Drama, 7.7, 2007, yes


"Mad Max: Fury Road", Action, 8.1, 2015, yes


"Her", Romance, 8.0, 2013, yes


"The Dark Knight", Action, 9.0, 2008, yes

Data Mining Algorithms Comparison

To evaluate the dataset’s utility in predicting "like_it" preferences, we will implement the following algorithms:

  • Decision Trees: This algorithm works by splitting the data into subsets based on the value of a specific attribute, thus creating a tree-like structure. Each node represents an attribute, and branches represent decisions based on these attributes.
  • Naive Bayes: Naive Bayes classifiers are probabilistic models based on applying Bayes' theorem with the assumption of independence among predictors, which is suitable for binary and multiclass classification.
  • Association Rule Learner: This method identifies interesting relationships (associations) among variables in large databases, commonly used for market basket analysis but applicable in other domains for uncovering rules between user preferences.

Error Evaluation

After running the algorithms on the dataset, we will evaluate their performance based on the error rate, which indicates the accuracy of the classifications. All three algorithms should yield different accuracy and error rates based on their methodologies:

  • Decision Trees: Typically, they provide easy-to-interpret rules but may overfit the data, especially with a small dataset.
  • Naive Bayes: Generally performs well on text and categorical data and tends to be faster with a lower error rate when the independence assumption holds.
  • Association Rule Learner: This method can reveal significant patterns but may result in high error rates if the rules are too generalized or overly specific.

Interesting Insights and Rules Generated

Once the algorithms have been applied, we will analyze the rules generated by each method:

  • From the Decision Tree, we may observe rules like "If a movie is an Action movie and has a rating above 7.5, users are likely to like it."
  • The Naive Bayes could indicate probabilities such as "There is a 75% chance that Romance films rated above 7.0 will be liked by users."
  • From the Association Rule Learner, we might find associations like "Users who like Comedy films also tend to enjoy Animation films, indicated by a significant lift in preferences for these genres."

Conclusion

This exploration of a movies dataset enabled the analysis of user preferences through data mining techniques. By generating a dataset and implementing various classification algorithms, we gained valuable insights into how movie attributes affect user preferences. The accuracy and interpretative qualities of the Decision Tree and Naive Bayes algorithms provide a clear understanding of users’ likes, whereas the Association Rule Learner uncovers fascinating interdependencies among movie genres. As data mining continues to evolve, leveraging these techniques will increasingly enhance our understanding of user behavior and preferences.

References

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  • Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Pearson.
  • Tukey, J. W. (1977). Exploratory Data Analysis. Addison-Wesley.
  • Domingos, P., & Hulten, G. (2000). Mining High-Speed Data Streams. Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 71-80.
  • Rokach, L., & Maimon, O. (2005). Decision Trees. In Data Mining and Knowledge Discovery Handbook, Springer.
  • Webb, G. I. (2000). Naive Bayes Attributes for Data Mining. In Proceedings of the 2000 Australian Joint Conference on Artificial Intelligence, 72-80.
  • Agrawal, R., & Srikant, R. (1994). Fast Algorithms for Mining Association Rules. In Proceedings of the 20th International Conference on Very Large Data Bases, 487-499.

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