Examples with Itemset de.lmu.ifi.dbs.elki.algorithm.itemsetmining.Itemset used on opensource projects

Example 1 with Itemset

use of de.lmu.ifi.dbs.elki.algorithm.itemsetmining.Itemset in project elki by elki-project.

the class FrequentItemsetsResult method writeToText.

@Override
public void writeToText(TextWriterStream out, String label) {
    StringBuilder buf = new StringBuilder();
    for (Itemset itemset : itemsets) {
        // Reuse
        buf.setLength(0);
        out.inlinePrintNoQuotes(itemset.appendTo(buf, meta));
        out.flush();
    }
}

Example 2 with Itemset

use of de.lmu.ifi.dbs.elki.algorithm.itemsetmining.Itemset in project elki by elki-project.

the class DiSHPreferenceVectorIndex method determinePreferenceVectorByApriori.

/**
 * Determines the preference vector with the apriori strategy.
 *
 * @param relation the database storing the objects
 * @param neighborIDs the list of ids of the neighbors in each dimension
 * @param msg a string buffer for debug messages
 * @return the preference vector
 */
private long[] determinePreferenceVectorByApriori(Relation<V> relation, ModifiableDBIDs[] neighborIDs, StringBuilder msg) {
    int dimensionality = neighborIDs.length;
    // database for apriori
    UpdatableDatabase apriori_db = new HashmapDatabase();
    SimpleTypeInformation<?> bitmeta = VectorFieldTypeInformation.typeRequest(BitVector.class, dimensionality, dimensionality);
    for (DBIDIter it = relation.iterDBIDs(); it.valid(); it.advance()) {
        long[] bits = BitsUtil.zero(dimensionality);
        boolean allFalse = true;
        for (int d = 0; d < dimensionality; d++) {
            if (neighborIDs[d].contains(it)) {
                BitsUtil.setI(bits, d);
                allFalse = false;
            }
        }
        if (!allFalse) {
            SingleObjectBundle oaa = new SingleObjectBundle();
            oaa.append(bitmeta, new BitVector(bits, dimensionality));
            apriori_db.insert(oaa);
        }
    }
    APRIORI apriori = new APRIORI(minpts);
    FrequentItemsetsResult aprioriResult = apriori.run(apriori_db);
    // result of apriori
    List<Itemset> frequentItemsets = aprioriResult.getItemsets();
    if (msg != null) {
        msg.append("\n Frequent itemsets: ").append(frequentItemsets);
    }
    int maxSupport = 0;
    int maxCardinality = 0;
    long[] preferenceVector = BitsUtil.zero(dimensionality);
    for (Itemset itemset : frequentItemsets) {
        if ((maxCardinality < itemset.length()) || (maxCardinality == itemset.length() && maxSupport == itemset.getSupport())) {
            preferenceVector = Itemset.toBitset(itemset, BitsUtil.zero(dimensionality));
            maxCardinality = itemset.length();
            maxSupport = itemset.getSupport();
        }
    }
    if (msg != null) {
        // 
        msg.append("\n preference ").append(// 
        BitsUtil.toStringLow(preferenceVector, dimensionality)).append('\n');
        LOG.debugFine(msg.toString());
    }
    return preferenceVector;
}