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Let D be the set of transactions represented in Table 10.1, where each transaction T in D represents a set of items contained in I . Suppose that we have a particular set of items A (e.g., beans and squash), and another set of items B (e.g., asparagus). Then an association rule takes the form if A, then B (i.e., A ? B), where the antecedent A and the consequent B are proper subsets of I , and A and B are mutually exclusive. This definition would exclude, for example, trivial rules such as if beans and squash, then beans. (more…)

There are two principal methods of representing this type of market basket data: using either the transactional data format or the tabular data format. The transactional data format requires only two fields, an ID field and a content field, with each record representing a single item only. For example, the data in Table 10.1 could be represented using transactional data format as shown in Table 10.2. (more…)

What types of algorithms can we apply to mine association rules from a particular data set? The daunting problem that awaits any such algorithm is the curse of dimensionality: The number of possible association rules grows exponentially in the number of attributes. Specifically, if there are k attributes, we limit ourselves to binary attributes, we account only for the positive cases (e.g., buy diapers = yes), there are on the order of k 2k?1 possible association rules. Consider that a typical application for association rules is market basket analysis and that there may be thousands of binary attributes (buy beer? buy popcorn? buy (more…)

Affinity analysis is the study of attributes or characteristics that go together. Methods for affinity analysis, also known as market basket analysis, seek to uncover associations among these attributes; that is, it seeks to uncover rules for quantifying the relationship between two or more attributes. Association rules take the form If antecedent, then consequent, along with a measure of the support and confidence associated with the rule. For example, a particular supermarket may find that of the 1000 customers (more…)

Cluster membership may be used to enrich the data set and improve model efficacy. Indeed, as data repositories continue to grow and the number of fields continues to increase, clustering has become a common method of dimension reduction. We will illustrate howcluster membership may be used as input for downstream data mining models, using the churn data set and the clusters uncovered above. Each
record now has associated with it a cluster membership assigned by the Kohonen networks algorithm. We (more…)

I hope you have enjoyed Discovering Knowledge in Data: An Introduction to Data Mining, and that our experience together has whetted your appetite for learning more about this unique and powerful field of study. In fact, it is true that we have only just begun our exploration of data mining. More volumes in thisWiley Interscience data mining series await your examination. (more…)

For busy people in this modern time like us, job is our life. We work and work whole weeks for the better future that we think we can get it. But, somehow, our performance are decreasing, and if this happen, we need some refreshing and relaxation activity to help restore our condition. And after that, we can get the best performance that we have. (more…)

Although it is easy to use the words imagination and suggestion interchangeably, there is an important distinction between them. Imagination is the ability to visualise to produce a mental impression of sights, sounds, smells, tastes and memories of touch. You can use imagination to create a strong visual link between two ideas you are trying to remember. Used in this way, it is a key to memory. (more…)

Cluster profiles may of themselves be of actionable benefit to companies and researchers. They may, for example, suggest marketing segmentation strategies in an era of shrinking budgets. Rather than targeting the entire customer base for a mass mailing, for example, perhaps only the most profitable customers may be targeted. (more…)

As we know that factors like management mistakes in your organization, tight market competition, or miss located outlets will presumably lead to great loss in business but still fail managing in financial sector is still the strongest factor why an organization will fall apart. Hence, with a great foundation and proper financial management, your business will be way more stable. It happens to all kind of business including Physical Therapy business. Since this business is very unique, it will be more complex to achieve proper financial management means balance cash flow. You can have balance (more…)