Temporal periodicity of patterns can be regarded as an important criterion for measuring the interestingness of frequent patterns in several applications. A frequent pattern can be said periodic-frequent if it appears at a regular interval. In this paper, we introduce the problem of mining the top-k periodic frequent patterns i.e. the periodic patterns with the k highest support. An efficient s...

A large number of web databases are only accessible through proprietary form-like interfaces which require users to query the system by entering desired values for a few attributes. A key restriction enforced by such an interface is the top-k output constraint i.e., when there are a large number of matching tuples, only a few (top-k) of them are preferentially selected and returned by the websi...

Selective search is a distributed retrieval technique that reduces the computational cost of large-scale information retrieval. By partitioning the collection into topical shards, and using a resource selection algorithm to identify a subset of shards to search, selective search allows retrieval effectiveness to be maintained while evaluating fewer postings, often resulting in 90+% reductions i...

In this paper, an optimized k nearest neighbor algorithm for the 2nd edition of the Large Scale Hierarchical Text Classification Pascal Challenge was summarized. Firstly, we perform k-NN algorithm on the datasets to obtain the top-k nearest neighbors for each testing documents. Secondly, several critical category-neighbors features were identified and the impact of each of those features were e...

The indoor localisation based on magnetic field (MF) has drawn much research attention since they have a range of applications in science and industry. position estimation is generally the Euclidean distance (ED) between compared data points. Commonly, state-of-the-art k-nearest neighbour (KNN) algorithm used to estimate test point (TP) by considering average location closest estimated K refere...

Data clustering is the process of partitioning a set of data objects into meaning clusters or groups. Due to the vast usage of clustering algorithms in many fields, a lot of research is still going on to find the best and efficient clustering algorithm. K-means is simple and easy to implement, but it suffers from initialization of cluster center and hence trapped in local optimum. In this paper...

Top-k queries, i.e. queries returning the top k results ordered by a user-defined scoring function, are an important category of queries. Order is an important property of data that can be exploited to speed up query processing. State-of-the-art SPARQL engines underuse order, and top-k queries are mostly managed with a materialize-then-sort processing scheme that computes all the matching solut...

A∗ parsing makes 1-best search efficient by suppressing unlikely 1-best items. Existing kbest extraction methods can efficiently search for top derivations, but only after an exhaustive 1-best pass. We present a unified algorithm for k-best A∗ parsing which preserves the efficiency of k-best extraction while giving the speed-ups of A∗ methods. Our algorithm produces optimal k-best parses under ...

Top-k (preference) queries are used in several domains to retrieve the set of k tuples that more closely match a given query. For high-dimensional spaces, evaluation of top-k queries is expensive, as data and space partitioning indices perform worse than sequential scan. An alternative approach is the use of sorted lists to speed up query evaluation. This approach extends performance gains when...