By Sanjay Madria, Takahiro Hara
This ebook constitutes the refereed complaints of the 18th overseas convention on facts Warehousing and data Discovery, DaWaK 2016, held in Porto, Portugal, September 2016.
The 25 revised complete papers offered have been rigorously reviewed and chosen from seventy three submissions. The papers are prepared in topical sections on Mining sizeable facts, purposes of huge information Mining, sizeable info Indexing and looking, monstrous facts studying and defense, Graph Databases and information Warehousing, facts Intelligence and Technology.
Read Online or Download Big Data Analytics and Knowledge Discovery: 18th International Conference, DaWaK 2016, Porto, Portugal, September 6-8, 2016, Proceedings PDF
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Extra resources for Big Data Analytics and Knowledge Discovery: 18th International Conference, DaWaK 2016, Porto, Portugal, September 6-8, 2016, Proceedings
As the number of iterations increases, the number of upper approximation computations decreases, thus accelerating convergence of the algorithm. The CCUA technique renders several rough clusters wherein an element may have multiple cluster memberships. First of all, we remove the redundant clusters, thus retaining all the unique clusters. These unique clusters might consist of some distinct clusters with minor overlap among their elements and some non-distinct clusters with high overlap among their elements.
2. TopPI and baseline run-times using 16 threads We also observe that the baseline enumerates many more intermediate solutions. Ideally, an algorithm would only enumerate outputted solutions. But, as shown in Sect. 4, item-centric mining requires the enumeration of a few additional itemsets to reach some solutions. 8 million distinct itemsets. 4 millions. As each D[i] is mined independently for all items i, the baseline cannot amortize results from a branch to another, so this result would likely be also observed with another top-k CIS mining algorithm.
Then it invokes, for each item i, startBranch(i , D, k ), which enumerates itemsets P such that max (P ) = i. In our examples, as in TopPI, items are 22 M. Kirchgessner et al. Algorithm 1. TopPI’s main function 1 2 3 4 5 Data: dataset D, integer k Result: Output top-k CIS for all items of D begin foreach i ∈ I do initialize top(i), heap of max size k foreach i ∈ I do startBranch(i, D, k) // Collector instantiation // In increasing item order represented by integers. While loading D, TopPI indexes items by decreasing frequency, hence 0 is the most frequent item.