Download Efficient Mining of Partial Periodic Patterns in Time Series by Han J., Dong G., Yin Y. PDF

By Han J., Dong G., Yin Y.

Partial periodicity seek, i.e., look for partial periodic styles in time-series databases, is a fascinating facts mining challenge. prior stories on periodicity seek commonly think about discovering complete periodic styles, the place each time limit contributes (precisely or nearly) to the periodicity. besides the fact that, partial periodicity is quite common in perform because it is much more likely that just some of the time episodes may well convey periodic patterns.We current a number of algorithms for effective mining of partial periodic styles, through exploring a few fascinating houses relating to partial periodicity, comparable to the Apriori estate and the max-subpattern hit set estate, and by means of shared mining of a number of sessions. The max-subpattern hit set estate is an important new estate which permits us to derive the counts of all widespread styles from a comparatively small subset of styles latest within the time sequence. We convey that mining partial periodicity wishes simply scans over the time sequence database, even for mining a number of sessions. The functionality examine exhibits our proposed equipment are very effective in mining lengthy periodic styles.

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Line Graphs Line graphs, where the location of each data point is marked along a line, were mentioned in the discussion of the mode. 1 Numbers Used to Summarize Data Meaures of Central Tendency Definition Symbol Advantages Disadvantages Mean Simple average X mathematically convenient unfair representation of center of highly skewed data more fair representation of center of highly skewed data mathematically inconvenient more fair representation of center of highly skewed discrete data not meaningful when data takes continuous or finely divided discrete values (µ) Median Half way along ordered data Mode Most common value Measures of Spread Range Gap between smallest and largest Variance average of the squares of the deviations from the mean (divide by n - 1 rather than n in taking the average) var Standard Deviation square root of variance Interquartile Range Range of values containing the middle half of the data Simple Reflects size of the sample as well as the underlying spread in the population Mathematically convenient.

For example, the probability of a die landing on either a "5" or a "6" is 1/6+ 1/6= 1/3. 34 Statistics with Common Sense As simple as these axioms are, they give rise to a large mathematical theory that can be shown to be compatible with both the "proportion of the time" and "measure of belief' approach to probability, but the details of this are beyond the scope of this book. PROBABILITIES OF COMBINATIONS OF EVENTS Often it is necessary to deal with the probabilities of combinations of events.

The expansion gives cross terms, just as the expansion of (a + b)2 gives the cross term lab as well as the more obvious terms a2 + b2. However, providing we are dealing with all possible combinations of values of/ and /*, the cross terms turn out to be zero for the same reason that the average of all positive and negative deviations about the average are zero. This page intentionally left blank CHAPTER 3 Basic Probability and Fisher's Exact Test To deal further with the variability and uncertainty that commonly arise whenever measurements are made, we need to know a little about the mathematical theory of chance: probability.

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