What is discriminative frequent pattern classification?

What is discriminative frequent pattern classification?

Frequent Pattern-Based Classification is learning a classification model in the feature space of single fea- tures as well as frequent patterns, where frequent pat- terns are generated w.r.t. min sup.

What is negative pattern mining?

Negative sequential pattern mining has attracted increasing concerns in recent data mining research because it considers negative relationships between itemsets, which are ignored by positive sequential pattern mining. However, the search space for mining negative patterns is much bigger than that for positive ones.

What is mining pattern?

In data mining: Pattern mining. Pattern mining concentrates on identifying rules that describe specific patterns within the data. Market-basket analysis, which identifies items that typically occur together in purchase transactions, was one of the first applications of data mining.

What is maximal pattern mining?

Mining maximal frequent patterns (MFPs) in transactional databases (TDBs) and dynamic data streams (DDSs) is substantially important for business intelligence. After removing null transactions and infrequent items, the resulting transformed dataset becomes denser compared to the original distributions.

What is classification by frequent pattern?

A frequent pattern is a generalization of the concept of a frequent item set used in association rule mining[1]. The first step in the algorithm is the construction of the collection of frequent patterns. Without any refining operation, those frequent patterns are used for classification.

Is an association based classification able to achieve more classification accuracy than decision tree method?

Associative classification achieves higher classification accuracy than some traditional rule-based classification approaches. However, the approach also has two major deficiencies. First, it generates a very large number of association classification rules, especially when the minimum support is set to be low.

What is a negative pattern?

Unlike self-defeating beliefs, negative thinking patterns are not always with you. Rather, they only surface when you are faced with an issue. Also known as cognitive distortions, these negative thoughts come to mind during times of stress and reinforce your self-defeating beliefs.

What is classifier in data mining?

Classifier: An algorithm that maps the input data to a specific category. Classification model: A classification model tries to draw some conclusion from the input values given for training. It will predict the class labels/categories for the new data.

What is a maximal itemset?

By definition, An itemset is maximal frequent if none of its immediate supersets is frequent. An itemset is closed if none of its immediate supersets has the same support as the itemset.

Which is the best description of pattern mining?

Graph pattern mining is the mining of frequent subgraphs (also called (sub)graph patterns) in one or a set of graphs. Methods for mining graph patterns can be categorized into Apriori-based and pattern growth–based approaches.

Which is the fastest way to mine discriminative patterns?

In this case, discriminative patterns are those defined to be patterns that have high information gain. (Hong Cheng later developed a spin off system called DDPMine that sped up her previous algorithm, but did not reduce classification scores.) What is the fastest way to mine them?

Is there a formal definition of discriminative patterns?

To be honest, there is no formal definition of discriminative patterns, unlike how there would be with frequent patterns, contrast patterns, and confidence. This paper right here actually takes the time to try describing different discriminative patterns, and understand their statistical value when finding them in datasets.

How to find discriminative patterns in dataset?

Once you define what a “good” discriminative pattern is, you could formulate a pattern mining algorithm around this definition, and seek these patterns in your dataset. Finding patterns in a dataset isn’t “unsupervised learning” per se.