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# Approaches to Other Data Mining Problems

1. Discovery of Sequential Patterns 2. Discovery of Patterns in Time Series 3. Regression 4. Neural Networks 5. Genetic Algorithms

Approaches to Other Data Mining Problems

1. Discovery of Sequential Patterns

The discovery of sequential patterns is based on the concept of a sequence of item-sets. We assume that transactions such as the supermarket-basket transactions we discussed previously are ordered by time of purchase. That ordering yields a sequence of itemsets. For example, {milk, bread, juice}, {bread, eggs}, {cookies, milk, coffee} may be such a sequence of itemsets based on three visits by the same customer to the store. The support for a sequence S of itemsets is the percentage of the given set U of sequences of which S is a subsequence. In this example, {milk, bread, juice} {bread, eggs} and {bread, eggs} {cookies, milk, coffee} are considered subsequences. The problem of identifying sequential patterns, then, is to find all subsequences from the given sets of sequences that have a user-defined minimum support. The sequence S1, S2, S3, ... is a predictor of the fact that a customer who buys itemset S1 is likely to buy itemset S2 and then S3, and so on. This prediction is based on the frequency (support) of this sequence in the past. Various algorithms have been investigated for sequence detection.

2. Discovery of Patterns in Time Series

Time series are sequences of events; each event may be a given fixed type of a trans-action. For example, the closing price of a stock or a fund is an event that occurs every weekday for each stock and fund. The sequence of these values per stock or fund constitutes a time series. For a time series, one may look for a variety of pat-terns by analyzing sequences and subsequences as we did above. For example, we might find the period during which the stock rose or held steady for n days, or we might find the longest period over which the stock had a fluctuation of no more than 1 percent over the previous closing price, or we might find the quarter during which the stock had the most percentage gain or percentage loss. Time series may be compared by establishing measures of similarity to identify companies whose stocks behave in a similar fashion. Analysis and mining of time series is an extended functionality of temporal data management (see Chapter 26).

3. Regression

Regression is a special application of the classification rule. If a classification rule is regarded as a function over the variables that maps these variables into a target class variable, the rule is called a regression rule. A general application of regression occurs when, instead of mapping a tuple of data from a relation to a specific class, the value of a variable is predicted based on that tuple. For example, consider a relation

LAB_TESTS (patient ID, test 1, test 2, ..., test n)

which contains values that are results from a series of n tests for one patient. The target variable that we wish to predict is P, the probability of survival of the patient. Then the rule for regression takes the form:

(test 1 in range1) and (test 2 in range2) and ... (test n in rangen) P = x, or x < P y

The choice depends on whether we can predict a unique value of P or a range of val-ues for P. If we regard P as a function:

P = f (test 1, test 2, ..., test n)

the function is called a regression function to predict P. In general, if the function appears as

Y = f (X1, X2, ..., Xn),

and f is linear in the domain variables xi, the process of deriving f from a given set of tuples for <X1, X2, ..., Xn, y> is called linear regression. Linear regression is a commonly used statistical technique for fitting a set of observations or points in n dimensions with the target variable y.

Regression analysis is a very common tool for analysis of data in many research domains. The discovery of the function to predict the target variable is equivalent to a data mining operation.

4. Neural Networks

A neural network is a technique derived from artificial intelligence research that uses generalized regression and provides an iterative method to carry it out. Neural networks use the curve-fitting approach to infer a function from a set of samples. This technique provides a learning approach; it is driven by a test sample that is used for the initial inference and learning. With this kind of learning method, responses to new inputs may be able to be interpolated from the known samples. This interpolation, however, depends on the world model (internal representation of the problem domain) developed by the learning method.

Neural networks can be broadly classified into two categories: supervised and unsupervised networks. Adaptive methods that attempt to reduce the output error are supervised learning methods, whereas those that develop internal representations without sample outputs are called unsupervised learning methods.

Neural networks self-adapt; that is, they learn from information about a specific problem. They perform well on classification tasks and are therefore useful in data mining. Yet, they are not without problems. Although they learn, they do not provide a good representation of what they have learned. Their outputs are highly quantitative and not easy to understand. As another limitation, the internal representations developed by neural networks are not unique. Also, in general, neural networks have trouble modeling time series data. Despite these shortcomings, they are popular and frequently used by several commercial vendors.

5. Genetic Algorithms

Genetic algorithms (GAs) are a class of randomized search procedures capable of adaptive and robust search over a wide range of search spacetopologies. Modeled after the adaptive emergence of biological species from evolutionary mechanisms, and introduced by Holland, GAs have been successfully applied in such diverse fields as image analysis, scheduling, and engineering design.

Genetic algorithms extend the idea from human genetics of the four-letter alphabet (based on the A,C,T,G nucleotides) of the human DNA code. The construction of a genetic algorithm involves devising an alphabet that encodes the solutions to the decision problem in terms of strings of that alphabet. Strings are equivalent to individuals. A fitness function defines which solutions can survive and which cannot. The ways in which solutions can be combined are patterned after the cross-over operation of cutting and combining strings from a father and a mother. An initial population of a well-varied population is provided, and a game of evolution is played in which mutations occur among strings. They combine to produce a new generation of individuals; the fittest individuals survive and mutate until a family of successful solutions develops.

The solutions produced by GAs are distinguished from most other search techniques by the following characteristics:

·        A GA search uses a set of solutions during each generation rather than a single solution.

·        The search in the string-space represents a much larger parallel search in the space of encoded solutions.

·        The memory of the search done is represented solely by the set of solutions available for a generation.

·        A genetic algorithm is a randomized algorithm since search mechanisms use probabilistic operators.

·        While progressing from one generation to the next, a GA finds near-optimal balance between knowledge acquisition and exploitation by manipulating encoded solutions.

Genetic algorithms are used for problem solving and clustering problems. Their ability to solve problems in parallel provides a powerful tool for data mining. The drawbacks of GAs include the large overproduction of individual solutions, the random character of the searching process, and the high demand on computer processing. In general, substantial computing power is required to achieve anything of significance with genetic algorithms.

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