Concepts inImproved smoothed analysis of the k-means method
Smoothed analysis
Smoothed analysis is a way of measuring the complexity of an algorithm. It gives a more realistic analysis of the practical performance of the algorithm, such as its running time, than using worst-case or average-case scenarios. For instance the simplex algorithm runs in exponential-time in the worst-case and yet in practice it is a very efficient algorithm. This was one of the main motivations for developing smoothed analysis.
more from Wikipedia
Chernoff bound
In probability theory, the Chernoff bound, named after Herman Chernoff, gives exponentially decreasing bounds on tail distributions of sums of independent random variables. It is better than the first or second moment based tail bounds such as Markov's inequality or Chebyshev inequality, which only yield power-law bounds on tail decay. It is related to the (historically earliest) Bernstein inequalities, and to Hoeffding's inequality. Let X1, ...
more from Wikipedia
Standard deviation
In statistics and probability theory, standard deviation (represented by the symbol ¿) shows how much variation or "dispersion" exists from the average (mean, or expected value). A low standard deviation indicates that the data points tend to be very close to the mean, whereas high standard deviation indicates that the data points are spread out over a large range of values.
more from Wikipedia
Normal distribution
In probability theory, the normal (or Gaussian) distribution is a continuous probability distribution that has a bell-shaped probability density function, known as the Gaussian function or informally the bell curve: The parameter ¿ is the mean or expectation (location of the peak) and ¿ is the variance. ¿ is known as the standard deviation. The distribution with ¿ = 0 and ¿ = 1 is called the standard normal distribution or the unit normal distribution.
more from Wikipedia
Independence (probability theory)
In probability theory, to say that two events are independent intuitively means that the occurrence of one event makes it neither more nor less probable that the other occurs. For example: The event of getting a 6 the first time a die is rolled and the event of getting a 6 the second time are independent. By contrast, the event of getting a 6 the first time a die is rolled and the event that the sum of the numbers seen on the first and second trials is 8 are not independent.
more from Wikipedia
Upper and lower bounds
In mathematics, especially in order theory, an upper bound of a subset S of some partially ordered set (P, ¿) is an element of P which is greater than or equal to every element of S. The term lower bound is defined dually as an element of P which is less than or equal to every element of S. A set with an upper bound is said to be bounded from above by that bound, a set with a lower bound is said to be bounded from below by that bound.
more from Wikipedia
Bounded set
"Bounded" and "boundary" are distinct concepts; for the latter see boundary (topology). A circle in isolation is a boundaryless bounded set, while the half plane is unbounded yet has a boundary. In mathematical analysis and related areas of mathematics, a set is called bounded, if it is, in a certain sense, of finite size. Conversely, a set which is not bounded is called unbounded. The word bounded makes no sense in a general topological space, without a metric.
more from Wikipedia
Time complexity
In computer science, the time complexity of an algorithm quantifies the amount of time taken by an algorithm to run as a function of the size of the input to the problem. The time complexity of an algorithm is commonly expressed using big O notation, which suppresses multiplicative constants and lower order terms. When expressed this way, the time complexity is said to be described asymptotically, i.e. , as the input size goes to infinity.
more from Wikipedia