As the amount of online text increases, the demand for text classification to aid the analysis and management of text is increasing. Text is cheap, but information, in the form of knowing what classes a text belongs to, is expensive. Automatic classification of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained from texts which have themselves been manually classified. In this paper we will discuss a procedure of classifying text using the concept of association rule of data mining. Association rule mining technique has been used to derive feature set from pre-classified text documents. Naïve Bayes classifier is then used on derived features for final classification. Text classification is the automated assigning of natural language texts to predefined categories based on their content. Text Classification is the primary requirement of Text Retrieval systems, which retrieve texts in response to a user query, and Text Understanding systems, which transform text in some way such as producing summaries, answering questions or extracting data. There exist some algorithms for learning to classify text based on the Naïve Bayes classifier. The probabilistic approaches for learning to classify text are described by Lewis (Lewis et al. 1992) . In applying Naïve Bayes classifier, each word position in a document is defined as an attribute and the value of that attribute to be the English word found in that position. Naïve Bayes classification is given by: V NB = argmax P (V j ) Õ P (a i½ V j ) To summarize, the Naïve Bayes classification V NB is the classification that maximizes the probability of observing the words that were actually found in the example documents, subject to the usual Naïve Bayes independence assumption. The first term can be estimated based on the fraction of each class in the training data. The following equation is used for estimating the second term: n k + 1 n + ½vocabulary½ where n is the total number of word positions in all training examples whose target value is V j , n k is the number of times that word is found among these n words positions, and ½vocabulary½is the total number of distinct words found within the training data. The proposed system is given a set of example documents. We first preprocess the text documents by parsing and removing stop words (Frank). We then collect frequently occurring words from each document. Each document is treated as a transaction and the set of frequently occurring words are viewed as a set of items in the transaction. We then apply association mining method (Frank, 2000) to discover sets of associated words in the documents. These set of associated words act as features. We them classify new documents using Naïve Bayes approach but using derived feature sets.  Data Mining Association rule mining finds interesting association or correlation relationships among a large set of data items. The discovery of interesting association relationships among huge amounts of transaction records can help in many decision making processes. We now define some of the terminologies. Rules that satisfy both a minimum support threshold (min_sup) and a minimum confidence threshold (min_conf) are called strong. A set of items is referred to as an itemset. An itemset that contains k items is a k-itemset. The occurrence frequency of an itemset is the number of transactions that contain the itemset. This is also known, simply, as the frequency, support count, or count of the itemset. An itemset satisfies minimum support if the occurrence frequency of the itemset is greater than or equal to the product of min_sup and the total number of transactions in D. The number of transactions required for the itemset to satisfy minimum support is therefore referred to as the minimum support count. If an itemset satisfies minimum support, then it is a frequent itemset. Apriori is an influential algorithm for mining frequent itemsets for Boolean association rules. The name of the algorithm is based on the fact that the algorithm uses prior knowledge of frequent itemset properties. Association rule mining is a two steps process. 1. Find all frequent itemsets: By definition, each of these itemsets will occur at least as frequently as a pre-defined minimum support count. 2. Generate strong Association rules from the frequent itemsets: By definition, these rules must satisfy minimum confidence. Apriori employes an iterative approach known as a level-wise search, where k-itemsets are used to explore (k+1)-itemsets. First, the set of frequent 1-itemsets is found. This set is denoted L 1 . L 1 is used to find L 2 , the set of frequent 2-itemsets, which is used to find L 3 , and so on, until no more frequent k-itemsets can be found. The finding of each L k requires one full scan of the database. An important property called Apriori property, based on the observation is that, if an itemset I is not frequent, that is, P(I ) < min_sup then if an item A is added to the itemset I, the resulting itemset (i.e., I È A) cannot occur more frequently than I. Therefore, I È A is not frequent either, that is, P(IÈ A) < min_sup. To understand how Apriori property is used in the algorithm, let us look at how L k-1 is used to find L k . A two-step process is followed, consisting of join and prune actions. To find L k , a set of candidate kitemsets is generated by joining L k-1 with itself. This set of candidates is denoted by C k . Let l 1 and l 2 be itemsets in L k-1 then l 1 and l 2 are joinable if their first (k-2) items are in common, i.e., (l 1 The prune step: C k is the superset of L k . A scan of the database to determine the count of each candidate in C k would result in the determination of L k (itemsets having a count no less than minimum support in C k ). But this scan and computation can be reduced by applying the Apriori property. Any (k-1)-itemset that is not frequent cannot be a subset of a frequent k-itemset. is not in L k-1 , then the candidate cannot be frequent either and so can be removed from C k .. Consider an example of Apriori, based on the following transaction database, D of Figure: 1, with 9 transactions, to illustrate Apriori algorithm. 1. In the first iteration of the algorithm, each item is a member of the set of candidate 1-itemsets, The algorithm simply scans all of the transactions in order to count the number of occurrences of each item. 2. If minimum support count is set to 2, frequent 1-itemsets, L 1 , can then be determined from candidate 1-itemsets satisfying minimum support. 3. To discover the set of frequent 2-itemsets, L 2 , the algorithm uses L 1 | L 1 to generate a candidate set of 2-itemsets ( Figure: 4) .
Research on text classification using the concept of association rule of data mining where naïve Bayes classifier was used to classify text and finally showed the dependability of the naïve Bayes classifier with associated rules REF . But since this method ignores the negative calculation for any specific class determination in some cases, accuracy may fall.
Text Classification using the Concept of Association Rule of Data Mining
Proc. International Conference on Information Technology, Kathmandu, Nepal, pp. 234-241, May. 2003
Computer Science